Today we are really excited to announce the launch of Voicegain Whisper, an optimized version of Open AI's Whisper Speech recognition/ASR model that runs on Voicegain managed cloud infrastructure and accessible using Voicegain APIs. Developers can use the same well-documented robust APIs and infrastructure that processes over 60 Million minutes of audio every month for leading enterprises like Samsung, Aetna and other innovative startups like Level.AI, Onvisource and DataOrb.
The Voicegain Whisper API is a robust and affordable batch Speech-to-Text API for developersa that are looking to integrate conversation transcripts with LLMs like GPT 3.5 and 4 (from Open AI) PaLM2 (from Google), Claude (from Anthropic), LLAMA 2 (Open Source from Meta), and their own private LLMs to power generative AI apps. Open AI open-sourced several versions of the Whisper models released. With today's release Voicegain supports Whisper-medium, Whisper-small and Whisper-base. Voicegain now supports transcription in over multiple languages that are supported by Whisper.
Here is a link to our product page
There are four main reasons for developers to use Voicegain Whisper over other offerings:
While developers can use Voicegain Whisper on our multi-tenant cloud offering, a big differentiator for Voicegain is our support for the Edge. The Voicegain platform has been architected and designed for single-tenant private cloud and datacenter deployment. In addition to the core deep-learning-based Speech-to-text model, our platform includes our REST API services, logging and monitoring systems, auto-scaling and offline task and queue management. Today the same APIs are enabling Voicegain to processes over 60 Million minutes a month. We can bring this practical real-world experience of running AI models at scale to our developer community.
Since the Voicegain platform is deployed on Kubernetes clusters, it is well suited for modern AI SaaS product companies and innovative enterprises that want to integrate with their private LLMs.
At Voicegain, we have optimized Whisper for higher throughput. As a result, we are able to offer access to the Whisper model at a price that is 40% lower than what Open AI offers.
Voicegain also offers critical features for contact centers and meetings. Our APIs support two-channel stereo audio - which is common in contact center recording systems. Word-level timestamps is another important feature that our API offers which is needed to map audio to text. There is another feature that we have for the Voicegain models - enhanced diarization models - which is a required feature for contact center and meeting use-cases - will soon be made available on Whisper.
We also offer premium support and uptime SLAs for our multi-tenant cloud offering. These APIs today process over 60 millions minutes of audio every month for our enterprise and startup customers.
OpenAI Whisper is an open-source automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. The architecture of the model is based on encoder-decoder transformers system and has shown significant performance improvement compared to previous models because it has been trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection.
Learn more about Voicegain Whisper by clicking here. Any developer - whether a one person startup or a large enterprise - can access Voicegain Whisper model by signing up for a free developer account. We offer 15,000 mins of free credits when you sign up today.
There are two ways to test Voicegain Whisper. They are outlined here. If you would like more information or if you have any questions, please drop us an email support@voicegain.ai
Voicegain has released its Speech Analytics (SA) API that supports variety of analytics tasks performed on the audio or the transcript of that audio. The features supported by Voicegain SA API were chosen to support our target main use case which is processing Call Center calls.
The current release supports offline Speech Analytics. The data that can be obtained through Speech Analytics API is listed below.
Note, here we do not include things that can be obtained also from our Transcribe API, like: transcript, decibel values, audiozones, etc. These, however, will be accessible from the Speech Analytics API response.
Per channel analytics:
Global analytics:
Real-time Speech Analytics will be available in the near future. Soon we also plan to release Score Card support for Speech Analytics.
Per channel analytics coming soon:
Additionally, we will soon support PII redaction of any named entity from either transcript or audio.
Speech Analytics API supports the following types of audio input:
You can see the API specification here.
In this blog post we present a unique feature of the Voicegain speech-to-text platform that efficiently combines the use of grammars with the use of large vocabulary models to provide developers with the ability to achieve high recognition accuracy in a very efficient and convenient way.
Speech recognition (ASR) systems generally can be divided into two types:
This type of recognizer is generally used for transcription where the vocabulary is very broad and the length of the speech audio is unlimited (except for practical e.g. resource related limit). Typical components and processing steps of such a system are illustrated below:
The working of such a system is as follows: (s) The audio signal is processed into features. (b) The features are fed into an acoustic model processor. The processor converts data from the acoustic realm to text/linguistic or some other intermediate (e.g. audio embeddings) realm. The output values may be phonemes, letters, word pieces, audio embeddings, etc., presented as vectors of probabilities. (c) These vectors are then passed to search/optimization component. Search uses the language model to decide which hypotheses formed from the output of the previous stage are most likely to be the correct textual interpretation of the input speech audio.
The Language Models used may take variety of forms. Two of the many possible manifestations are: (a) ARPA language models, which are n-gram based, and (b) Neural Network language models where a neural network (e.g., RNN) is trained to represent a language model. Some of the Language models can also incorporate a decoder part, if the acoustic model output is encoded (e.g. if it is represented by acoustic embedding).
Because the vocabulary of this type of recognizers is large, they are prone to misrecognitions. This is particularly the case for short utterances that do not provide much context for the language model to sufficiently constrain the hypotheses. An example would be misrecognizing “card” as “car” if that is the only word that is said and a speaker has a specific accent.
Cloud speech-to-text offerings from the Big Cloud providers - Google, Amazon, and Microsoft are all examples of Large Vocabulary ASRs.
In such a system, the Voice Bot/IVR developer uses a context free grammar to define a set of possible utterances that can be recognized. The grammars are typically defined using the SRGS (Speech Recognition Grammar Specification) standard - either ABNF or GRXML grammar. Other types of grammars used are JSGF (JSpeech Grammar Format) and GSL (which is Nuance Grammar Specification Language).
Components and processing steps of a typical speech recognition system that uses such grammars are illustrated below:
In this system the evaluation of the output from acoustic model processing is done by a search/optimizer that uses the rules contained in the grammar to decide which hypotheses are acceptable. Only the utterances that can be generated from the grammar may be output.
If an utterance outside of the grammar is spoken and presented to the recognizer it may still be recognized but with low confidence. If the confidence is below a set threshold a NOMATCH will be returned.
The obvious disadvantage of using such a recognizer is that it will not recognize utterances outside the scope of grammar. Such utterances are called Out-of-Grammar utterances. However, a big advantage with this approach is that it will be less prone to misrecognition when an utterance that is spoken has been anticipated and is included in the grammar.
An additional advantage of using a grammar-based recognizer is that most grammars allow for insertion of semantic tags, which allow the grammar to not only define an utterance but also the semantic interpretation of that utterance.
Examples of such a grammar-based speech recognition system would the speech-to-text offerings like Nuance ASR or Lumenvox ASR.
Clearly both types of speech recognition systems have advantages and disadvantages. It hence seems understandable that a combination of both could potentially have the advantages of both while possibly avoiding some disadvantages.
A simple approach would be to combine two different speech recognition systems. One would need to create two speech recognition sessions and split the incoming audio stream so that each session is fed a copy of incoming audio. Those two sessions would process the audio separately and would output separate results that would then need to be combined. This is illustrated below:
The setup as presented above has several disadvantages:
Voicegain platform provides a speech recognition system that combines both types of speech recognition to benefit from the advantages of both. Our system is illustrated in the figure below:
In this system the processing up to the output from the Acoustic model processing is essentially identical to the processing done in systems depicted in the first two figures of this post. However, after that step Voicegain includes a novel Search/Optimization module that uses both grammar and the large vocabulary language model to generate the final recognition results. The end-pointing is performed in a way that is similar to grammar-based recognizer as that seems to make most sense given the use case (but this can be modified). The final recognition result will comprise n-best results from the grammar-based recognition, if the grammar did MATCH, and one or more hypotheses from the large vocabulary recognition.
The application developer may make own decisions as to how to use the recognition result. For example, the confidence value may be used to determine whether the grammar-based result or the large vocabulary result should be used at a given point in the application.
With Voicegain’s release of 1.22.0 , this feature is Generally Available as part of our Recognize API.
An example request using our /asr/recognize/async API looks like this:
As you can see there is just one definition for the incoming audio stream. The grammar section of settings.asr contains two grammar definitions:
In addition to being available in our STT API and Telephone Bot API the ability to support both gramma-based and large vocabulary recognition at the same time is supported via the MRCP interface. For example, from VXML you can pass both GRXML grammar and builtin:speech/transcribe grammar and you will receive both GRXML result and large vocabulary result.
If you are building an Intelligent Voice Assistant, Voice Bot, Speech IVR Application or any other application that could benefit from this feature, please contact us via (email info@voicegain.ai) to engage in a more in-depth discussion.
Most enterprise IT organizations have mature telephony based IVR applications that serve as the “front door” for all voice based customer support calls. These applications use a combination of touchtone (DTMF) and speech to interact with callers. They have been carefully designed, developed and tuned over the years.
The objectives of any IVR are two fold 1) Automate simple routine queries (like balance inquiry, payment status, etc) and 2) Authenticate and intelligently route calls that require live support to the appropriate agent.
IT organizations across industry verticals like financial services, travel, media, telecom, retail or health-care have a small staff of in-house or outsourced IVR developers to maintain these applications. While enterprises have been focused on scaling and upgrading their digital support channels (like chat and email), IVR applications have largely remained un-touched for years.
As CIOs and CDOs (Chief Digital Officers) embark on strategic initiatives to migrate enterprise workloads to the Cloud, one "niche" workload on this list is the IVR. However migrating IVRs "as-is" to the cloud is tricky. The languages, protocols and platforms that these telephony based IVRs were built on is from the early 2000s and are approaching obsolescence. Also while they support directed dialogs with limited customer spoken utterances, they are not a good fit for conversational bot interactions.
So IT organizations are faced with a Catch 22 situation. On one-hand, it is cumbersome to maintain these IVR workloads. On the other hand, the rationale to migrate existing platforms "as-is" to modern cloud infrastructure is questionable. Why bear the trouble and expense if IVRs are eventually are going to be replaced by conversational bots?
So there is a real need to modernize these IVRs as part of their cloud migration strategy.
Traditionally speech IVR applications ran on on-premise Contact Center telephony platforms. Companies like Avaya, Nortel, Cisco, Intervoice, Genesys and Aspect dominated the vendor landscape. In the early to mid-2000s, these vendors worked collaboratively as part of the W3C consortium to develop VoiceXML, an open vendor agnostic language for speech-enabled IVR applications.
VoiceXML enabled developers to build interactive voice dialogs and provided a standard way to interact with an automatic speech recognizer (ASR). This was done using a telephony based protocol called MRCP. The standard also provided a method to define speech grammars called SRGS and a format called GRXML.
The architecture and supporting jargon/terminology around VoiceXML borrowed heavily from the web world. The VoiceXML platform was referred to as a “Voice browser” that could “render VoiceXML pages” just like how a web browser could render HTML pages. Most contact center platforms provided visual IDEs to help build and maintain these interactive call flows. Some also automated the generation of the VoiceXML pages. The IDE generated code that could run on application server (like Apache Tomcat) which in turn generated VoiceXML pages that were sent to a VoiceXML platform over standard HTTP. The application server was also responsible for making web-services requests to enterprise database resources that were required for the IVR interaction; for e.g. billing/payment systems or CRM systems.
Also most ASRs from the late 90s and early 2000s were based on Hidden Markov Models and Gaussian Mixture models. They mainly supported grammar-based recognition - which meant that as a Speech IVR developer you had to anticipate all possible utterances that a user could say in response to a question/prompt. There were some options to build open-ended statistical language models but these were tricky and required careful selection of the training corpus.
Why modernize now?
While VoiceXML worked well in the past, it is a niche and outdated language. The last release of VoiceXML 2.1 was back in 2007!! That is more than a decade ago.
And a lot has changed in the web world since then. VoiceXML was developed at a time when JSP (Java Server Pages) was widely used. So it was before JSON, YAML, RESTful APIs & AJAX.
For enterprises, it is expensive to maintain a dedicated staff - whether in-house or outsourced - with niche skills in technologies like VoiceXML and MRCP.
Enterprises should ideally be able to run IVR app like any other modern web application. Most enterprise web apps are built on programming languages like Python, Node.JS that are popular with web developers. They are containerized using docker and orchestrated using Kubernetes.
It would be ideal for an enterprise IT organization for its IVR app to be built on similar programming languages so that it can be supported or maintained just like other applications in the IT portfolio.
In addition to the obsolescence of VoiceXML, the speech recognition engine (ASR) that was deployed in the early 2000s has also become outdated. Modern speech-to-text engines are built on Deep Neural Networks that run on powerful GPU infrastructure. They offer amazing accuracy and allow the use of a very large vocabulary - which is what is needed for bot like conversational experience. Also modern NLU engines allow you to easily extract intents from the transcribed text.
So if an enterprise wants to offer a voice bot that supports an open conversational experience, they need to move to a modern DNN based Speech-to-Text platform that can integrate with such NLU engines.
At Voicegain, we recommend that an enterprise first modernize the underlying infrastructure while retaining the existing IVR application logic. This is a great first step. It allows an enterprise to continue serving existing users while taking a step towards providing a more conversational user experience.
We suggest that the existing call flow logic - which is typically maintained using visual IDEs of contact center platforms - get rewritten (ideally with the help of automated tools) into a modern programming language like Python or Node.Js.
Instead of generating legacy VoiceXML pages, enterprises should use web friendly data representation languages like JSON or YAML to interact with modern RESTful Speech-to-Text APIs using web callbacks.
How Voicegain supports IVR App modernization?
At Voicegain, we provide a modern Voice AI platform that includes
Voicegain is developing tools to automatically convert VoiceXML to equivalent JSON/YAML representation that talks to our callback APIs.
How is this a "future proof" architecture for an enterprise?
The Voicegain platform is capable of large vocabulary transcription which is a requirement for NLU based Voice Bots. This will be the way customers interact with enterprises in the future.
We allow developers to switch between grammar based recognition and large vocabulary recognition at each and every turn of the dialog; or you could simultaneously use both to achieve more flexibility.
Our Telephony Bot APIs can also integrate with Bot Frameworks like Google Dialog Flow, .
We are inviting enterprise web developers for a free trial of our platform.
Digital Transformation efforts in most enterprises have only gained pace as a result of the pandemic. The maxim going around in corporate circles in 2020 (and very likely to continue in 2021) is that the coronavirus was the real Chief Digital Officer (CDO) for most enterprises!! CIOs, CTOs and the CDOs today have stronger and bolder mandates to fundamentally alter the economics of their businesses.
They are increasingly being asked by their CEOs to make big bets and take on initiatives that can "materially" transform the underlying economics of their businesses.
A significant area of focus for digital enterprises is what is being referred to as "Practical AI". How businesses use AI and ML in a practical yet fundamental manner to transform themselves? Enterprises in different industries - financial services, travel, telecommunications, media and retail - are realizing that investing in strong AI & ML capabilities in their teams is critical to their post-pandemic digital future. In many Fortune 1000 companies, businesses are 'insourcing' and aggressively hiring AI & ML teams even as they outsource maintenance of legacy back-end systems to gain competitive advantage.
And one of the most practical AI applications in the enterprise is Voice AI - which refers to the use of AI & ML on voice conversations within the enterprise.
Despite the proliferation of digital channels like chat/text messaging, email and social, higher value sales conversations, meetings, and involved customer service discussions are conducted pre-dominantly over voice. Speaking is not just more efficient than typing, it is also more engaging!! The human touch with voice is something that we as humans will always value. Voice is here to stay and its enduring significance is as immutable as the laws of gravity!
So what is changing in the world of Voice? It is just that the underlying plumbing is transforming - voice conversations traditionally took place over legacy telephony networks. They are quickly moving to meeting platforms like Zoom, Microsoft Teams and Webex; so a voice only conversation is being replaced by a richer voice & video conversation conducted over the internet.
The barriers historically associated with voice - costs and complexity of voice infrastructure- have been eliminated with technologies like WebRTC, 4G/5G and cloud computing. For consumers, the cost of making a voice call is now zero - it is the cost of their WiFi or 4G/5G bandwidth (as consumers use free mobile apps like Facetime, Skype and WhatsApp).
Voice AI is highly accurate Speech-to-Text and NLU that is built on highly specialized and customizable (trainable) Deep Neural Networks running on GPUs.
What is unique about Deep Neural Networks is that the underlying Speech-to-Text and NLU models can be trained - easily and affordably - on enterprise specific datasets. You can leverage enterprise's lexicon and corpus - both voice & text. So instead of a 'one-size-fits-all approach', each enterprise can have its own Voice AI infrastructure - that is trained on its product names, industry jargon, employee & customer names, unique accents etc. Once it is trained, there are two big applications - 1) Voice AI for Automation and using 2) Voice AI for Analytics.
Enterprises can build Voice bots to intelligently respond to contact requests from their prospects and customers anytime anywhere. Voice Bots may also be used to respond to internal employees queries in a service/help desk context. The automation use-case is one that has really accelerated during the pandemic. Bots can help businesses deal with massive disruption caused by everyone - in sales, customer success and service - working from home during the pandemic. McKinsey has written about automation using AI.
Voice AI also makes it possible for businesses to transcribe 100% of their voice conversations and subsequently mine the text for sentiment and analytics/insights.
With Voice AI, businesses can ensure that its frontline sales staff is able to pitch its core value proposition, benefits, product and service features in a consistent and compelling manner. This can be a massive boost to sales teams as they can improve conversion ratios and accurately forecast pipeline with Voice AI.
Voice AI can also ensure that customer success and service personnel are provided with tailored/customized insights to improve not just their efficiency (metrics like AHT in contact center) and but also enhance effectiveness measures like CSAT and NPS scores.
At Voicegain, we are passionate about helping enterprises, small and mid-size businesses, entrepreneurs and startup companies with their Voice AI efforts. Our mission is to build the world's most open developer friendly Voice AI platform. Be a part of our mission by signing up here. You can transcribe your calls/meetings, try out our APIs, building amazing telephony bots and more !
About the Author:
Arun Santhebennur is the Co-founder & CEO of Voicegain. To have a more in-depth conversation, please connect with Arun on LinkedIn or send us an email.
Developers building voice-enabled SaaS applications that embed Speech-to-Text or Transcription as part of their product have multiple vendors to choose from.
However, the decision to pick the right Speech-to-Text platform or API is rather involved. This writeup outlines three types of vendors and the three key criteria (summarized as the 3 As - Accuracy, Affordability and Accessibility) to consider while making that choice.
Most voice-enabled SaaS apps that incorporate Speech-to-Text APIs broadly fall into two categories 1) Analytics and 2) Automation.
Whether you are developing an analytics app or an automation app, developers have the following vendor choices.
There are 3 distinct types of vendors
The first set of choices for most developers are Speech-to-Text APIs from the big cloud companies - Google, Amazon and Microsoft. These big players offer Speech-to-Text APIs as part of their portfolio of Cloud AI & ML services. The strategy for the Big Cloud providers is to sell their entire stack - from cloud infrastructure to APIs and even products.
However the Cloud service providers may compete directly with the developers they look to serve. E.g. Amazon Connect directly competes with Contact Center platforms that are hosted on AWS. Google Dialogflow directly competes with other NLU startups that may be looking to build and offer Voice bots and Voice Assistants to enterprises.
Other than the big 3, Nuance and IBM Watson are large companies that have a rich history of providing Automated Speech Recognition (ASR). Of the two, Nuance is better known and has been a dominant player both in the enterprise call center market with its Nuance ASR engine and in the medical transcription space with its Dragon offering. IBM has a long history of fundamental speech recognition and IBM Watson Speech-to-Text is their developer oriented offering.
Voicegain.ai, our company, plays alongside other startup companies like Deepgram that target SaaS developers with their best-of-breed DNN based speech-to-text. Since these startups are specialized providers, they are focused on beating the big cloud providers and legacy players with respect to price, performance and ease of use.
The key criteria while picking an ASR or Speech-to-Text platform are the 3 As - Accuracy, Affordability and Accessibility.
The first and most important criteria for any Speech-to-Text platform is recognition accuracy. However accuracy is a tricky metric to assess and measure. There is no 'one-size-fits-all' approach to accuracy. We have shared our thoughts & benchmarks here. While Voicegain matches or exceeds the "out-of-the-box" transcription accuracy of most of the larger players, we suggest that you do additional diligence before making a choice. The audio datasets used in these benchmarks may or may not be similar to the use case or context for which the developer intends to use the API.
While accuracy is usually measured using Word Error Rate (WER), it is important to note that this metric too has limitations. For a SaaS app, getting some important and critical words right may be even more important than just a low overall WER.
That being said, it is important for developers to establish and calculate a quick baseline "out-of-the-box" accuracy for their application with their audio datasets.
At Voicegain, we have open sourced tools to benchmark our performance against the very best in business. We strongly recommend that developers & ML Engineers calculate a benchmark baseline accuracy for their vendor choices using a statistically significant volume of audio datasets for their application.
From a developer perspective, a baseline accuracy measure will provide insights into the how closely your datasets match the datasets that the underlying STT models from the vendors have been trained on.
Here are a set of important factors that may affect your "out-of-the-box" accuracy:
Developers also need to establish a "Target" accuracy that their SaaS application or product requires. Usually Product Managers determine this based on their needs.
It is possible to bridge the gap between the Target Accuracy and the Baseline "out-of-the-box" accuracy. While it is outside the scope of this post, here is an overview of some ways in which developers can improve upon the Baseline accuracy.
However not all Speech-to-Text platforms support one or more of these options.
At Voicegain.ai, we support all the above options. Picking the right approach involves a more in-depth technical conversation. We invite you to get in touch with us.
To summarize, the choice may not be as simple as picking the one with the best "out-of-the-box' accuracy. It could in fact be a platform that provides the most convenient and least expensive path to bridge the gap between Target and Baseline accuracy.
The second most important factor after accuracy is price. Most SaaS products are very disruptively priced. It is not uncommon for the SaaS product to be sold at 'tens of dollars' ($35-100) per user per month. It is critical that Speech-to-Text APIs make up as small a fraction of the SaaS price as possible. The price directly impacts the "gross-margin" of the SaaS application, a critical financial metric/KPI that SaaS companies care dearly about.
In addition to the top-line usage based price for the platform, it is also important to understand what the minimum billable time and billing increment for each interaction. Many of the large Cloud providers have a very high minimum billable times - 12 or 18 seconds. This makes it very expensive for Voice Bots or Voice Assistant.
Another cost related aspect is the price for transcribing multi-channel audio, where only one speaker is active at the time. Does the platform charge for transcribing silence on the inactive channel ?
The last (but not the least!) important criterion is how accessible - or in other words how simple and easy is it to integrate the Speech-to-Text platform with the SaaS Application.
This ease of integration becomes even more important if the SaaS Application streams audio real-time to the Speech-to-Text platform. Another important criterion for real-time streaming is latency - which is the time to receive recognition results from the platform. For a Bot or Voice Assistant, it is important to get API latency down to 500 milliseconds or lower. Also, reliable and fast end-of-speech detection is crucial in those scenarios for natural dialog turn taking.
At Voicegain, we support multiple options - ranging from TCP-based methods like gRPC and Websockets to telephony/UDP protocols like SIP/RTP, MRCP and SIPREC.
The choice made by the developer depends on the following factors:
In conclusion, selecting the right Speech-to-Text or ASR platform for a SaaS application is a diligent exercise; it is by no means a slam dunk!!
1. Click here for instructions to access our live demo site.
2. If you are building a cool voice app and you are looking to test our APIs, click hereto sign up for a developer account and receive $50 in free credits
3. If you want to take Voicegain as your own AI Transcription Assistant to meetings, click here.
Voicegain Speech-to-Text and Speech Analytics platform supports SIPREC protocol as one of the ways an audio stream of a telephone call can be fed to the speech recognizer.
The Session Recording Protocol (SIPREC) is an open SIP-based protocol for call recording. The standard is defined by Internet Engineering Task Force. It is supported by many phone platforms and call recording system vendors.
The SIPREC standard defines a protocol used to interact between a Session Recording Client (the role generally performed by PBX system or Session Border Controller) and a Session Recording Server (a third party call recorder, in our case a Voicegain-provided SIPREC server). SIPREC opens two RTP streams (one for inbound and one for outbound audio of the call) to the Recording Server. SIPREC protocol also is able to transfer call metadata to the Recorder, this is important so that the recordings can be tied to the information about the calls.
SIPREC is usually used for call recording but the standard essentially provides a real-time audio stream from the telephone call which makes it suitable for applications which have to work real-time like, e.g., agent assist or agent monitoring. Using the SIPREC interface Voicegain can provide real-time transcript of the call as well as perform speech analytics tasks in real time, e.g., keyword and phrase detection, personally-identifiable information scrubbing, sentiment and mood estimation, named-entity recognition, and variety of metrics (like silence, overtalk, etc.).
Audio obtained via SIPREC can also be recorded and transcribed, analyzed, or retrieved at a later time.
Voicegain SIPREC interface has been tested with the following platforms:
Voicegain can capture relevant call metadata in addition to obtaining the audio (the metadata capture functionality may differ in capabilities depending on the client platform).
Voicegain platform can be configured to automatically launch transcription and speech-analytics as soon as the new SIPREC session gets established.
The output from transcription and speech analytics is available via a Web API. We also support websockets for more convenient streaming of the transcription and/or speech analytics data. SIPREC support is available both in the Cloud and the Edge (OnPrem) deployments of the Voicegain Platform.
SIPREC is an Enterprise feature of the Voicegain platform and is not included in the base package. Please contact support@voicegain.ai or submit a Zendesk ticket for more information about SIPREC and if you would like to use it with your existing Voicegain account.
Genesys Voice Platform does not support SIPREC directly. However, it does support streaming of the inbound and outbound RTP media to two separate SIP endpoints - the end result being pretty much the same as if SIPREC was used. We are currently working on implementing support for this feature of the Genesys Voice Platform for real-time audio streaming to Voicegain Platform. It should be available in Q1 2021.
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Read more →Interested in customizing the ASR or deploying Voicegain on your infrastructure?
Voicegain helps developers build awesome voice enabled apps by providing them with the most accurate, affordable and accessible Speech-to-Text platform.
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