Developing Speech-to-Text Applications with Azure Cognitive Services SDK

Developing Speech-to-Text Applications with Azure Cognitive Services SDK

Table of Contents

1. Introduction
2. Exploring the SDK
3. Creating a Speech-to-Text Application
4. Using the REST API
5. Integrating with Language Understanding Intelligent Service (LUIS)
6. Supported Languages and Platforms
7. Configuring the Speech-to-Text SDK
8. Using Different Audio Inputs
9. Creating a Custom Audio Input Stream
10. Transcribing Speech to Text
11. Conclusion

Introduction

In this module, we will learn how to develop for the Speech-to-Text service. This Tutorial focuses on using the SDK and REST API provided by the service. We will explore the different classes and their interactions, and also cover some C# examples. Additionally, we'll discuss the multilingual and multi-platform capabilities of the SDK.

Exploring the SDK

The SDK provides developers with the functionality to transcribe speech into text. It allows for the transcribing of short utterances of less than 15 seconds using the REST API or the SDK. However, with the SDK, developers can also transcribe longer utterances and even transcribe streaming audio. The SDK also enables integration with the Language Understanding Intelligent Service (LUIS) to derive intents and entities from the audio.

Creating a Speech-to-Text Application

To create a speech-to-text application, we will first need to explore the SDK. We'll go through the different classes and their functionalities, with a focus on C# examples. Afterwards, we'll create a quick speech-to-text application using the C# SDK. This will allow us to understand the basic workflow and concepts involved in developing for the service.

Using the REST API

In addition to the SDK, we can also interact with the speech-to-text service through a simple HTTP REST API. We will cover how to integrate with the service using this API. We'll go over the API endpoints and parameters required to transcribe speech. To demonstrate the usage of the REST API, we'll perform a demo using Postman.

Integrating with Language Understanding Intelligent Service (LUIS)

The speech-to-text service can seamlessly integrate with the Language Understanding Intelligent Service (LUIS). By using LUIS, developers can derive intents and entities from the transcribed speech. This section will provide an overview of how to use LUIS along with the speech-to-text service and demonstrate the capabilities it provides.

Supported Languages and Platforms

The speech-to-text SDK supports multiple languages and platforms. We'll focus on the C# SDK as the reference for functionality, but other supported languages have a similar interface. The C# SDK runs on the .NET Framework on Windows and also has multi-platform support for .NET Core. Additionally, it supports the Universal Windows Platform (UWP) and the Unity engine.

Configuring the Speech-to-Text SDK

Before using the speech-to-text SDK, we need to create a speech configuration for ease of use. The configuration includes parameters such as subscription key and service region. This section will explain how to create a speech configuration and use it to create a recognizer for speech to text. We'll also cover different options for microphone input and audio file input.

Using Different Audio Inputs

In addition to the default OS microphone, the speech-to-text SDK allows for using different audio inputs. This section will explain how to select audio input from devices other than the default microphone. We'll cover the process for different platforms such as Windows, Linux, and iOS. We'll also discuss using Bluetooth headsets with speech-enabled apps.

Creating a Custom Audio Input Stream

Developers can create their own custom audio input stream to interface with the speech-to-text SDK. This section will explain how to create a custom audio input stream that meets the required format specified by the SDK. We'll cover the necessary steps to create the stream and how to configure it for use with the speech recognizer.

Transcribing Speech to Text

Finally, we'll use the recognizer to convert speech to text. We'll invoke the recognizer's asynchronous method to transcribe a short utterance. Once the Transcription is complete, we'll handle the various result reasons such as "RecognizedSpeech", "NoMatch", and "Canceled". This section will provide examples and guidelines for handling these result reasons effectively.

Conclusion

In conclusion, this module has provided an in-depth exploration of the speech-to-text service, including the SDK and REST API. We have covered the process of creating a speech-to-text application, integrating with LUIS, and using different audio inputs. By following the steps and examples provided, developers can harness the power of speech-to-text capabilities in their applications and services.