Initializes a new instance of the SpeechRecognitionEngine class using the default speech recognizer for a specified locale. Initializes a new instance of the SpeechRecognitionEngine using the information in a RecognizerInfo object to specify the recognizer to use.
Initializes a new instance of the SpeechRecognitionEngine class with a string parameter that specifies the name of the recognizer to use. You can construct a SpeechRecognitionEngine instance from any of the following:. A specific recognition engine that meets the criteria that you specify in a RecognizerInfo object.
Before the speech recognizer can begin recognition, you must load at least one speech recognition grammar and configure the input for the recognizer. Before the speech recognizer can begin speech recognition, you must load at least one recognition grammar and configure the input for the recognizer. None of the installed speech recognizers support the specified locale, or culture is the invariant culture.
Culture is null. The following example shows part of a console application that demonstrates basic speech recognition, and initializes a speech recognizer for the en-US locale.
In Windows 7 and Vista Ultimate, you can obtain the above languages, free of charge, by installing the appropriate language pack. However, there are a number of ways you can install the speech engine:.
Click here for details on how to activate the speech engine and make it available to Pronunciation Coach. The wizard will automatically launch the Microphone Setup wizard. Removes the selected profile. The selected profile must not be in use by any other program when you choose to delete it. Starts the Speech Recognition Voice Training wizard. The wizard can help improve recognition accuracy by learning about your specific speaking style and the sounds of your environment.
This can help improve recognition accuracy. Sets Speech Recognition to start in sleep mode and allows Speech Recognition to enter sleep mode when you say "stop listening. Sets the number of spaces to insert after sentence-ending punctuation when you're dictating text using Speech Recognition. Allows you to set a preferred audio device, such as an audio input for Speech Recognition.
It will be active only if there is at least one audio device installed. Starts the Microphone Setup wizard. This helps you to calibrate the audio input devices and speaker levels.
Lists available text-to-speech voices. Click a voice to activate it. Once selected, the text-to-speech engine will speak the text to preview the voice. Shows additional information or options about the text-to-speech engine.
It is specific to the engine type, and not all engines have additional properties. Displays the sample text spoken by the text-to-speech playback voice. You can change the text temporarily, but it will always default to the original content.
Such solutions are very compact, but unfortunately sound quite mechanical. So, as with musical synthesizers, the focus gradually shifted to solutions based on samples, which require significant space, but essentially sound natural. To build such a system, you have to have many hours of high-quality recordings of a professional actor reading specially constructed text. This text is split into units, labeled and stored into a database.
Speech generation becomes a task of selecting proper units and gluing them together. If you need both male and female voices or must provide regional accents say, Scottish or Irish , they have to be recorded separately.
And the actors must read in a neutral tone to make concatenation easier. Splitting and labeling are also non-trivial tasks. It used to be done manually, taking weeks of tedious work. Thankfully, machine learning is now being applied to this. Unit size is probably the most important parameter for a TTS system. Obviously, by using whole sentences, we could make the most natural sounds even with correct prosody, but recording and storing that much data is impossible.
Can we split it into words? Probably, but how long will it take for an actor to read an entire dictionary? And what database size limitations are we facing? So usually units are selected as two three-letter groups. Now the last step. Having a database of speech units, we need to deal with concatenation. Alas, no matter how neutral the intonation was in the original recording, connecting units still requires adjustments to avoid jumps in volume, frequency and phase.
This is done with digital signal processing DSP. It can also be used to add some intonation to phrases, like raising or lowering the generated voice for assertions or questions. In this article I covered only the. Other platforms provide similar functionality. For cross-platform ecosystems like Python, there are some bridges like Pyttsx, but they usually have certain limitations. Cloud vendors, on the other hand, target wide audiences, and offer services for most popular languages and platforms.
While functionality is comparable across vendors, support for SSML tags can differ, so check documentation before choosing a solution. Microsoft offers a Text-to-Speech service as part of Cognitive Services bit.
It not only gives you 75 voices in 45 languages, but also allows you to create your own voices. For that, the service needs audio files with a corresponding transcript. You can write your text first then have someone read it, or take an existing recording and write its transcript.
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