Wir freuen uns, die Verfügbarkeit von deutscher Spracherkennung auf der Voicegain-Plattform bekannt zu geben. Es ist die dritte Sprache, die Voicegain nach Englisch und Spanisch unterstützt.

Die Spracherkennungsgenauigkeit des deutschen Modells hängt von der Art des Sprachaudios ab. Im Allgemeinen liegen wir nur wenige Prozent hinter der Genauigkeit zurück, die die Speech-to-Text-Engines von Amazon oder Google bieten. Der Vorteil unseres Spracherkennung ist der deutlich niedrigere Preis sowie die Möglichkeit, kundenspezifische Akustikmodelle zu trainieren. Benutzerdefinierte Modelle können eine höhere Genauigkeit aufweisen als Amazon oder Google. Wir empfehlen Ihnen, unsere Webkonsole und / oder API zu verwenden, um die tatsächliche Leistung Ihrer eigenen Daten zu testen.  

Natürlich bietet die Voicegain-Plattform auch andere Vorteile wie die Unterstützung von Edge-Bereitstellung (on-prem) und eine umfangreiche API mit vielen Optionen für die sofort einsatzbereite Integration in z. Telefonieumgebungen.

Derzeit ist unsere Speech-to-Text-API mit dem deutschen Modell voll funktionsfähig. Einige der Speech Analytics-API-Funktionen sind für Deutsch noch nicht verfügbar, z. B. Named Entity Recognition oder Sentiment / Mood Detection.

Das deutsche Modell ist zunächst nur in der Version verfügbar, die die Offline-Transkription unterstützt. Die Echtzeitversion des Modells wird in naher Zukunft verfügbar sein.

Um der API mitzuteilen, dass Sie das deutsche Akustikmodell verwenden möchten, müssen Sie es nur in den Kontexteinstellungen auswählen. Deutsche Modelle haben 'de' im Namen, z. VoiceGain-ol-de: 1

Wenn Sie die deutsche Sprachausgabe verwenden möchten, senden Sie uns bitte eine E-Mail an support@voicegain.ai. Wir werden sie für Ihr Konto aktivieren. Wenn Ihre Anwendung ein Echtzeitmodell erfordert, teilen Sie uns dies bitte ebenfalls mit.

We are pleased to announce availability of German Speech-to-Text on the Voicegain Platform. It is the third language that Voicegain supports after English and Spanish.

The recognition accuracy of the German model depends on the type of speech audio. Generally, we are just a few % behind the accuracy offered by the Speech-to-Text engines of the larger players (Amazon, Google, etc). The advantage of our recognizer is its affordability, ability to train customized acoustic models and deploy it in the datacenter or VPC. Custom models can have accuracy higher than that of Amazon or Google. We also offer extensive support for integrating with telephony.

We encourage you to sign up for a developer account and use our Web Console and/or our APIs to test the real-life performance on your own data.

Currently, our Speech-to-Text API supports the German Model. Currently the German Model supports off-line transcription. Real-time/Streaming version of the Model will be available in the near future.

To use the German Acoustic Model in Voicegain Web Console, select "de" under Languages in the Speech Recognition settings.

Voicegain STT platform has supported MRCP (Media Resource Control Protocol) for a long time now. Our ASR can be accessed using MRCP and we support both grammar-based recognition (e.g. GRXML) and large-vocabulary transcription. MRCP is a communication protocol designed to connect telephony based IVRs and Voice Bots with speech recognizers (ASR) and speech synthesizers (TTS).

Previously we tested connecting to Voicegain using MRCP from VXML platforms like Dialogic PowerMedia XMS or Aspect Prophecy. We had not tested connecting from FreeSWITCH, a popular open source telephony platform, using its MRCP plugin mod_unimrcp.

We are pleased to announce that Voicegain platform works out-of-the box with mod_unimrcp, the MRCP plugin for FreeSWITCH. However, getting the mod_unimrcp plugin to work on FreeSWITCH is not particularly trivial. Here are some pointers to help those who would like to use mod_unimrcp with our platform.

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Deploying Voicegain unimrcp server

There are currently 2 options to do this. We plan to add a third option very soon  

1. For production deployments of Speech IVRs and Voice Bots on FreeSWITCH, we recommend an Edge Deployment of the Voicegain platform. This will deploy our unimrcp server that can communicate with a locally deployed FreeSWITCH using MRCP.
2. To use our Cloud ASR, you will need to download a MRCP IVR Proxy. This proxy can be downloaded from the Voicegain Web Console. You will download a tar file that has the definition of a docker compose that you can then run on your docker server. This will deploy our preconfigured unimrcp server with a proxy for connecting to Voicegain Cloud Speech-to-Text engine .
3. (Coming soon) We plan to implement a voicegain_asr plugin that can be deployed on a standard unimrcp server. The plugin will talk to our ASR in the cloud using gRPC.

Also, the current TTS option accessible over MRCP are not great. Our focus has been on the use of prerecorded prompts for IVRs and Voice Bots. We plan to shortly allow developers to access the Google or Amazon TTS.

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Configuring FreeSWITCH for mod_unimrcp

mod_unimrcp does not get built by default when you build FreeSWITCH from source. To get it built you need to enable it in build/modules.conf.in by uncommenting this line: #asr_tts/mod_unimrcp

After the build, before starting FreeSWITCH you will need to:

• Add <load module="mod_unimrcp"/> to autoload_configs/modules.conf.xml(you can put it in <!-- ASR /TTS --> section because that is where it logically belongs)
• Create mrcp_profile for voicegain (see below)
• Modify content of autoload_config/unimrcp.conf.xmlIf you want to use both ASR and TTS via Voicegain MRCP, you will need to point both default-asr-profile and default-tts-profile to the voicegain1-mrcp2 profile you will create in mrcp_profiles folder.

Here is an example MRCP v2 profile for connecting to Voicegain MRCP:

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Here are some additional notes about the configuration file:

• It is important that the port range used by the Unimrcp Client:<param name="rtp-port-min" value="4000"/><param name="rtp-port-max" value="5000"/>is accessible from outside, otherwise, the TTS via MRCP will not work. Also, these ports may not overlap with the UDP ports used by FreeSWITCH.
• In some setups the "auto" values of :<param name="client-ip" value="auto"/> and<param name="rtp-ip" value="auto"/>may not work and you will have to manually specify the external IP.

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How to use mod_unimrcp

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Here is an example of how to play a question prompt and to invoke the ASR via mod_unimrcp to recognize a spoken phone number:

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session:execute("set", "tts_engine=unimrcp:voicegain1-mrcp2");
session:execute("set", "tts_voice=Catherine");
session:execute("play_and_detect_speech", 
"say:What is your phone number detect:unimrcp {start-input-timers=false,define-grammar=true,no-input-timeout=5000}builtin:grammar/phone")

asrResult = session:getVariable("detect_speech_result");
test

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What this example does is:

• tells FS which tts_egine to use
• sets the TTS voice - currently ignored
• plays a question prompt using the specified TTS and launches the recognition
• retrieves the result of the speech recognition

The result of the recognition is a string in XML format (NLSML). You will need to parse it to get the utterance and any semantic interpretations. NLSML result also contains confidence.  

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The normal command "play_and_detect_speech" holds onto ASR session until the end of the call - this makes subsequent recognitions more responsive, but you are paying for the MRCP session. You can also use this command "play_and_detect_speech_close_asr" to release ASR session immediately after recognition.

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If you have any questions about the use of Voicegain ASR via MRCP please contact us at: support@voicegain.ai

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Coming Soon

On our roadmap we have a mod_voicegain plugin for FreeSWITCH which will bypass the need for mod_unimrcp and unimrcp server and will be talking from FreeSWITCH directly to the Voicegain ASR using gRPC.

As pandemic forces Contact Centers to operate with work-from-home agents, managers are increasingly looking to real-time speech analytics to drive improvements in agent efficiency (via reduction in AHT) and effectiveness (improvements in FCR, NPS) and achieve 100% compliance.

Before the pandemic, Contact Center managers relied on a combination of in person supervision and speech analytics of recorded calls to drive improvements in agent efficiency and effectiveness.

However the pandemic has upended everything. It has forced contact centers to support work-from-home agents from multiple locations.  Team Leads who "walked the floor" and monitored  and assisted agents in realtime are not available any more. The offline Speech Analytics process - which is still available remotely - is limited and manual. A Call Coach or a QA Analyst coaches an agent manually using a sample 1-2% of the calls that have been transcribed and analyzed.

There is a now an urgent need to monitor and support agents real-time and provide them all tools and support that they had while they worked in their offices.

Real-time Agent Assist is the use of Artificial Intelligence - more specifically Speech Recognition and Natural Language Processing - to help agents real-time during the call in the following ways.

1. Agents can be presented with knowledge-base articles and next-best actions from intents that are extracted from the transcribed text
2. Using NLU algorithms and intents extracted, you can now summarize the call automatically and realize savings on disposition/wrap time
3. Supervisors can monitor sentiment real-time

Real-time Agent Assist can reduce AHT by 30 seconds to 1 minute, improve FCR by 3-5% and improve NPS/CSAT.

What does it take to implement Real-time Agent Assist?

Real-time agent assist involves the realtime transcription of the Agent and Caller Interaction and extracting keywords, insights and intents from the transcribed text and make it available in a user-friendly manner to both the Agents and also the team-leads and supervisors.

There are 4 key steps involved:

1. Audio Capture: The first step is to stream the two channels of audio (i.e agent and caller streams) from the Contact Center Platform that the client is using (whether premise based or cloud based). Voicegain supports a variety of protocols to stream audio. We have described them here and here.   We have integrated with premise-based major contact center platforms like Avaya, Cisco and Genesys. We have also integrated with Media Stream APIs from programmable CCaaS platforms like Twilio and SignalWire.
2. Transcription: The next step in the process is to transcribe the audio streams into text . Voicegain offers Transcription APIs to convert the audio into text realtime. We can stream the text realtime (using web-sockets or gRPC) so that it can be easily integrated into any NLU Engine.
3. NLU/Text Analytics:  In this step, the NLU engine extracts the intents from the transcribed text. These intents are trained in an earlier phase using phrases and sentences. Voicegain integrates with leading NLU Engines like RASA, Google Dialogflow, Amazon Lex and Salesforce Einstein.
4. Integration with Agent Desktop: The last and final step is to integrate the results of the NLU with the Agent Desktop.

At Voicegain, we make it really easy to develop real-time agent assist applications . Sign up to test the accuracy of our real-time model.

Outbound IVRs on Voicegain

Voicegain platform makes it easy to build IVRs for simple outbound calling applications like: surveys (Voice-of-Customer, political, etc), reminders (e.g. appointments, payments due), notifications (e.g. school closure, water boil notice), and so on.

Voicegain allows developers to use the outbound calling features of CPaaS platforms like Twilio or SignalWire with the speech recognition and IVR features of the Voicegain platform. All you need is this simple piece of code to make an outbound call using Twilio and connect it to Voicegain for IVR.

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Defining IVRs in declarative way

Voicegain provides a full featured Telephone Bot API. It is a webhook/callback style API that can be used in similar way you would use Twilio's TwiML. You can read more about it here

However, in this post, we describe an even simpler method to build IVRs. We allow developers to specify the Outbound IVR call flow definitions in a simple YAML format. We also provide a python script that can be easily deployed on AWS Lambda or on your web-server to interpret this YAML file. The complete code with examples can be found on our github. It is under MIT license so you can modify the main interpreter script to your liking. You might want to do it e.g. to make calls to external webservices that your IVR needs.

In this YAML format, an IVR question would be defined as follows:

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As you can see, this is a pretty easy way to define an IVR question. Notice also that we provide a built-in handling for the NOINPUT and NOMATCH re-prompts, as well as the logic for confirmations. This greatly reduces the the clutter in the specification as those flow scenarios do not have to be handled explicitly.

The questions support either use of grammars to map responses to semantic meaning, or they can alternatively simply capture the response using a large vocabulary transcription.

Prompts are played using TTS or can be concatenated from prerecorded clips.

Wait, there is more.

Because this is built on top of Voicegain Telephone Bot API it comes with full API access to the IVR call session. You can obtain details, including all the events and responses, of the complete session using the API. This includes the 2-channel recording plus also full transcription of both channels and also Speech Analytics features.

You can also examine the details of the session from the Voicegain Console and listen to the audio. This helps in testing the application before it gets deployed.  

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If you have questions about building this type of IVRs running on Voicegain platform, please contact us at support@voicegain.ai

Among the various speech-to-text APIs that Voicegain provides is a speech recognition API that uses grammars and supports continuous recognition. This API is ideally suitable for use in warehouse Voice Picking applications. Warehouse Management Systems can embed Voicegain APIs to offer Voice Picking as part of their feature set.

Here are more details of that specific API:

• Audio input - supports streaming of audio via websockets for very easy integration with web based or Android/iOS applications (gRPC support is in beta)
• Results of recognition are available via websocket or http callbacks in JSON format. Sending recognition results over websockets is a recent addition and it makes building web based voice picking applications much easier.
• Supports grammar based recognition - better suited for a well defined set of commands compared to large-vocabulary speech-to-text. Has higher accuracy, better noise rejection, better handling of various accents, etc. Using grammas provides a benefit of fast end-pointing - the recognizer knows that the command has been completely uttered and there is no additional timeout needed to determine end-of-speech.We support a variant of JSGF grammar format which is very intuitive and easy to use.
• Supports continuous recognition - multiple commands can be recognized in a single http session. Continuous recognition allows for the commands to be spaced closer together and allows for natural correction of misrecognitions by simple repetition.

In addition to that Voicegain Speech-to-Text platform provides additional benefits for Voice Picking applications:

• Acoustic/language model is customizable - this allows for very high recognition accuracy for specific domains
• Web-based tools available for reviewing utterance recognitions. These tools allow for tuning of grammars and for collection of utterances for model training.

Together this allows for your Voice Picking application to continually learn and improve.

Our APIs are available in the Cloud but can also be hosted at the Edge (on-prem) which can increase reliability and reduce the already low latencies.

If you would like to test our API and see how they would fit in your warehouse applications you can start with the fully functional example web app that we have made available on github: platform/examples/command-grammar-web-app at master · voicegain/platform (github.com)

If you have any question please email us at info@voicegain.ai. You can also sign-up for a free account on Voicegain Platform via our Web Console at: https://console.voicegain.ai/signup