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Watson Speech to Text review

Find out how ibm does speech recognition in our watson speech to text review.

TechRadar Verdict

There’s plenty to be said in favor of IBM’s Watson Speech to Text service, such as its ability to convert hours of audio into text quickly and accurately. But price, integration complexity, and somewhat patchy BETA features may put some businesses off.

Fast and accurate speech recognition

Grammar, language, and acoustic model training

More expensive than AWS or Google

Multi-speaker recognition is hit-and-miss

Why you can trust TechRadar We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

Watson is IBM’s natural-language-processing computer system. It powers the famous question-answering supercomputer as well as a series of AI-based enterprise products, including Watson Speech to Text . In our Watson Speech to Text review, we’ll take a look at one of the best speech-to-text apps around, ideal for anyone who wants to convert audio to text at scale.

The Watson speech processing platform is available on IBM Cloud. It’s a versatile tool and can be used in many contexts including dictation and conference call transcription. What’s more, unlike most other speech-to-text apps, it’s available as an API, allowing developers to embed it into voice control systems, among other things. 

Watson Speech to Text: Plans and pricing

You can use Watson Speech to Text to process up to 500 minutes of audio for free per month. If you want to convert more than that, you’ll need to pay for each audio minute, and the rate changes based on the duration of audio processed. Costs range from $0.01 to $0.02 per minute, and there’s an add-on charge of $0.03 per minute if you require IBM’s Custom Language Model. Premium quote-only Watson plans are available too, and these grant access to enhanced data privacy features and uptime guarantees.

You can also access the Watson Speech to Text system through a general-purpose IBM Cloud subscription. Natural language processing is just one app in a wide range of AI services you can get through IBM Cloud, so this is a good option for any organization that needs access to high-speed data transfers, chatbots, or text-to-speech tools.  

Watson Speech to Text: Features

Thanks to flexible API integration and other pre-build IBM tools, the Watson speech recognition service goes well beyond basic transcription. If you want to use it in a customer service context, for example, the Watson Assistant can be set up to process natural language questions directly or answer queries over the phone.

Watson works with live audio in 11 languages and can import sounds in a variety of pre-recorded formats. When streaming, real-time diagnostic support means Watson can prompt users to move closer to their microphone or change their environment. Also impressive is the fact that Watson can distinguish between different speakers in a shared conversation thanks to Speaker Diarization, a feature still undergoing beta testing.

Watson Speech to Text: Setup

To use Watson, the first thing you need to do is create an IBM Bluemix account. Registration is free and painless, requiring just an email address and password. Once logged in you need to add a provision on your account for the Speech to Text service. You’ll be given a couple of credentials at this stage that you should save in your own records. 

After you’ve done that, things get significantly more complex. To access Watson, you’ll need to add those credentials to a batch of client uniform resource locator (cURL) code and then run it on your machine. To find out exactly what command to call, check out this handy guide. Alternatively, if you just want to see how well the Watson system works without having to jump through all those hoops you can try it out on IBM’s demo site instead.

Watson Speech to Text: Interface

Unlike consumer-facing voice-to-text apps, Watson’s services are designed to be accessed through APIs and code embedded in other systems. For this reason, there’s no real Watson “interface”. Instead, Watson can be accessed through three different internet protocols. These are WebSockets, REST API, and Watson Developer Cloud.

To control Watson, you will need to use a command-line tool that connects to IBM’s cloud via one of those three routes. The interface that the end-user interacting with Watson sees will need to be built by someone on your development team separately. 

Watson Speech to Text: Performance

Overall, we were impressed by the way that this natural-language-processing platform handled real speech. We used Watson to transcribe clips we recorded in a range of challenging environments as well as soundbites of famous speeches given in several of Watson’s 11 supported languages.

Although errors grew more frequent for clips with lots of background noise, in general, Watson produced incredibly accurate results. We’d estimate from our tests that unprompted mistakes occurred only once every 150 words on average. However, it did become clear why Watson’s Speaker Diarization feature remains in BETA testing as, several times during our evaluation, one voice was mislabelled as separate speakers.

 Watson Speech to Text: Support 

The IBM resource center offers plenty of documentation to better understand how to apply Watson to your particular use case. It’s also worth making use of the API-integrations and SDKs created by the Watson developer community and posted to GitHub .

If you don’t find the solution to your problem there, you can reach out to IBM directly by opening a support ticket or contacting them over the phone. As long as you opted for one of the premium Watson packages, your Watson use will be protected by a Service Level Uptime agreement.

Watson Speech to Text: Final verdict

If your organization has the know-how and resources to properly integrate the IBM Watson Speech to Text platform into your system, you’ll benefit from advanced functions like real-time sound environment diagnostics and interim transcription results. However, small businesses and organizations will struggle with the technical challenge of setting Watson up properly.

The competition

The IBM Watson Speech to Text service is a direct competitor to bulk transcription services Google Cloud Speech-to-Text and Amazon Transcribe. Both of these are significantly cheaper than Watson, with Google Cloud transcription, for example, starting at $0.006 per minute. All three services share similar functions, such as customized vocabulary, but one feature sorely missing from IBM Watson but available with both competitors is automatic punctuation recognition.

Looking for another spoeech-to-text solution? Check out our Best speech-to-text software guide.

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Speech To Text using IBM Watson Studio

IBM Watson Studio is an integrated environment designed to develop, train, manage models, and deploy AI-powered applications and is a Software as a Service (SaaS) solution delivered on the IBM Cloud. The IBM Cloud provides lots of services like Speech To Text, Text To Speech, Visual Recognition, Natural Language Classifier, Language Translator, etc.

The Speech to Text service transcribes audio to text to enable speech transcription capabilities for applications.

Create an instance of the service

• Go to the Speech to Text page in the IBM Cloud Catalog.
• Sign up for a free IBM Cloud account or log in.
• Click Create .

Copy the Credentials to Authenticate to your service instance

• From the IBM Cloud Resource list , click on your Speech to Text service instance to go to the Speech to Text service dashboard page.
• On the Manage page, click Show Credentials to view your credentials.
• Copy the API Key and URL values.

Module Needed:

Now you’re ready to use the IBM Cloud Services.

Below code illustrates the use of IBM Watson studio’s Speech To Text Service using Python and web socket interface

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SpeechToTextV1 4.3.0 Docs (83% documented)

Speech to Text

• IBM Watson Speech to Text - API Reference
• IBM Watson Speech to Text - Documentation
• IBM Watson Speech to Text - Service Page

The IBM Watson Speech to Text service enables you to add speech transcription capabilities to your application. It uses machine intelligence to combine information about grammar and language structure to generate an accurate transcription. Transcriptions are supported for various audio formats and languages.

IMPORTANT: Please be sure to include both SpeechToTextV1.framework and Starscream.framework in your application. Starscream is a recursive dependency that adds support for WebSockets sessions.

The following example shows how to transcribe an audio file using the standard API endpoint.

For details on all API operations, including Swift examples, see the API reference.

Recognizing audio using websockets

The Swift SDK extends the Starcream library to offer websocket support for recognizing audio.

You can transcribe audio using web sockets by calling the recognizeUsingWebSockets method on the SpeechToText class, as shown in this example.

In the above example, the RecognitionSettings struct is used to define the settings for the recognition request. Additional the RecognizeCallback struct allows you to register event handlers for web socket events.

Recognizing audio from microphone

If you’d like to record audio from a device microphone and trascribe it using Speech to Text, you can use the SDK provided recognizeMicrophone method, as shown in this example.

The above example uses the recognizeMicrophone method within a function, which can be called from a button click or user input. How you call it is up to you, but the above provides a typical example of how you would record while responding to a UI action.

Advanced Usage

Microphone audio and compression in detail.

The Speech to Text framework makes it easy to perform speech recognition with microphone audio. The framework internally manages the microphone, starting and stopping it with various method calls ( recognizeMicrophone and stopRecognizeMicrophone , or startMicrophone and stopMicrophone ).

There are two different ways that your app can determine when to stop the microphone:

User Interaction: Your app could rely on user input to stop the microphone. For example, you could use a button to start/stop transcribing, or you could require users to press-and-hold a button to start/stop transcribing.

Final Result: Each transcription result has a final property that is true when the audio stream is complete or a timeout has occurred. By watching for the final property, your app can stop the microphone after determining when the user has finished speaking.

To reduce latency and bandwidth, the microphone audio is compressed to OggOpus format by default. To disable compression, set the compress parameter to false .

It’s important to specify the correct audio format for recognition requests that use the microphone:

Recognition Results Accumulator in detail

The Speech to Text service may not always return the entire transcription in a single response. Instead, the transcription may be streamed over multiple responses, each with a chunk of the overall results. This is especially common for long audio files, since the entire transcription may contain a significant amount of text.

To help combine multiple responses, the Swift SDK provides a SpeechRecognitionResultsAccumulator object. The accumulator tracks results as they are added and maintains several useful instance variables: - results : A list of all accumulated recognition results. - speakerLabels : A list of all accumulated speaker labels. - bestTranscript : A concatenation of transcripts with the greatest confidence.

To use the accumulator, initialize an instance of the object then add results as you receive them:

Session Management and Advanced Features

Advanced users may want more customizability than provided by the SpeechToText class. The SpeechToTextSession class exposes more control over the WebSockets connection and also includes several advanced features for accessing the microphone. The SpeechToTextSession class also allows users more control over the AVAudioSession shared instance. Before using SpeechToTextSession , it’s helpful to be familiar with the Speech to Text WebSocket interface .

The following steps describe how to execute a recognition request with SpeechToTextSession :

• Connect: Invoke connect() to connect to the service.
• Start Recognition Request: Invoke startRequest(settings:) to start a recognition request.
• Send Audio: Invoke recognize(audio:) or startMicrophone(compress:) / stopMicrophone() to send audio to the service.
• Stop Recognition Request: Invoke stopRequest() to end the recognition request. If the recognition request is already stopped, then sending a stop message will have no effect.
• Disconnect: Invoke disconnect() to wait for any remaining results to be received and then disconnect from the service.

All text and data messages sent by SpeechToTextSession are queued, with the exception of connect() which immediately connects to the server. The queue ensures that the messages are sent in-order and also buffers messages while waiting for a connection to be established. This behavior is generally transparent.

A SpeechToTextSession also provides several (optional) callbacks. The callbacks can be used to learn about the state of the session or access microphone data.

• onConnect : Invoked when the session connects to the Speech to Text service.
• onMicrophoneData : Invoked with microphone audio when a recording audio queue buffer has been filled. If microphone audio is being compressed, then the audio data is in OggOpus format. If uncompressed, then the audio data is in 16-bit PCM format at 16 kHz.
• onPowerData : Invoked every 0.025s when recording with the average dB power of the microphone.
• onResults : Invoked when transcription results are received for a recognition request.
• onError : Invoked when an error or warning occurs.
• onDisconnect : Invoked when the session disconnects from the Speech to Text service.

Note that the AVAudioSession.sharedInstance() must be configured to allow microphone access when using SpeechToTextSession . This allows users to set a particular configuration for the AVAudioSession . An example configuration is shown in the code below.

The following example demonstrates how to use SpeechToTextSession to transcribe microphone audio:

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