Speech Recognition in Xamarin.iOS - Xamarin (2023)

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This article introduces the new Speech API and shows how to implement it in a Xamarin.iOS app to support continuous speech recognition and transcribe speech (from live or recorded audio streams) to text.

New in iOS 10, Apple released the Speech Recognition API, which allows an iOS app to support continuous speech recognition and transcribe speech (from live or recorded audio streams) into text.

According to Apple, the Speech Recognition API has the following features and benefits:

  • Very accurate
  • state of the art
  • Easy to use
  • Fast
  • Supports multiple languages
  • Respects users' privacy

How speech recognition works

Speech recognition is implemented in an iOS app by capturing either live or pre-recorded audio (in any of the spoken languages ​​supported by the API) and passing it to a speech recognizer, which returns a plain-text transcription of the spoken words.

Keyboard dictation

When most users think of speech recognition on an iOS device, they think of the built-in Siri voice assistant that was released with the iPhone 4S along with keyboard dictation in iOS 5.

Keyboard dictation is supported by any interface element that supports TextKit (egUITextFieldorUITextArea) and is activated by the user clicking the dictation button (just to the left of the spacebar) on the iOS virtual keyboard.

Apple has released the following keyboard dictation statistics (collected since 2011):

  • Keyboard dictation has been widely used since its release in iOS 5.
  • Around 65,000 apps use it every day.
  • About a third of all iOS dictations are performed in a third-party app.

Keyboard Dictation is extremely easy to use as it requires no effort on the part of the developer other than using a TextKit UI element in the app's UI design. Keyboard dictation also has the benefit of not requiring any special permission requests from the app before it can be used.

Apps using the new Speech Recognition APIs must grant special permissions from the user, since Speech Recognition requires the transmission and temporary storage of data on Apple's servers. Please see oursSecurity and privacy improvementsdocumentation for details.

(Video) Text To Speech in Xamarin.Forms and .NET MAUI with Essentials

Although keyboard dictation is easy to implement, it comes with some limitations and disadvantages:

  • It requires the use of a text entry field and the display of a keyboard.
  • It only works with live audio input and the app has no control over the audio recording process.
  • It offers no control over the language used to interpret the user's speech.
  • The app has no way of knowing if the dictation key is even available to the user.
  • The app cannot customize the audio recording process.
  • It provides a very flat set of results that lack information such as timing and confidence.

Speech Recognition API

New in iOS 10, Apple released the Speech Recognition API, which provides a more powerful way for an iOS app to implement speech recognition. This API is the same one Apple uses to power both Siri and keyboard dictation, and it's capable of providing fast transcription with cutting-edge accuracy.

The results provided by the Speech Recognition API are transparently tailored to each user without the app having to collect or access private user data.

Speech Recognition API returns results to the calling app in near real-time while the user is speaking, providing more information about the translation results than just text. These include:

  • Multiple interpretations of what the user said.
  • Confidence levels for each translation.
  • timing information.

As mentioned above, audio for translation can be provided from either a live feed or from a pre-recorded source and in any of the 50+ languages ​​and dialects supported by iOS 10.

The Speech Recognition API can be used on any iOS device running iOS 10 and in most cases requires an active internet connection as most of the translations take place on Apple's servers. However, some newer iOS devices support continuous on-device translation of certain languages.

Apple has integrated an availability API to determine if a specific language is currently available for translation. The app should use this API instead of directly testing for internet connection itself.

As noted in the Keyboard Dictation section above, speech recognition requires the transmission and temporary storage of data on Apple's servers over the Internet, and therefore the appmustRequest the user's permission to perform the detection by including theNSSpeechRecognitionUsageDescriptionkey in hisInfo.plistFile and calling theSFSpeechRecognizer.RequestAuthorizationMethod.

Based on the audio source used for speech recognition, other changes to the appInfo.plistFile may be required. Please see oursSecurity and privacy improvementsdocumentation for details.

Introducing speech recognition in an app

There are four key steps the developer needs to take to adopt speech recognition in an iOS app:

  • Provide a usage description in the appInfo.plistfile with theNSSpeechRecognitionUsageDescriptionButton. For example, a camera app might contain the following description:"This way you can take a picture just by saying the word 'cheese'."
  • Request authorization by callingSFSpeechRecognizer.RequestAuthorizationMethod to present an explanation (provided in theNSSpeechRecognitionUsageDescriptionKey above), why the app wants the user to access speech recognition, in a dialog box, and allow them to accept or decline.
  • Create a speech recognition request:
    • For pre-recorded audio on disk, use theSFSpeechURLRecognitionRequestClass.
    • For live audio (or audio from memory), use theSFSPeechAudioBufferRecognitionRequestClass.
  • Pass the speech recognition request to a speech recognizer (SFSpeechRecognizer) to start detection. The app can optionally capture the returned dataSFSpeechRecognitionTaskto monitor and track detection results.

These steps are detailed below.

Providing a Description of Use

To provide what is requiredNSSpeechRecognitionUsageDescriptionkey onInfo.plistfile, proceed as follows:

(Video) Xamarin Forms Text To Speech (Android & iOS Text To Speech Application) App

  • Visual Studio for Mac
  • Studio visuals
  1. Double-click theInfo.plistfile to open it for editing.

  2. Switch toThoseView:

  3. Click onAdd a new entry, inputNSSpeechRecognitionUsageDescriptionfor theProperty,linefor theTypand aUsage Descriptionas theWert. For example:

  4. If the app is to handle live audio transcription, a description of microphone usage is also required. Click onAdd a new entry, inputNSMicrophoneUsageDescriptionfor theProperty,linefor theTypand aUsage Descriptionas theWert. For example:

  5. Save the changes in the file.


Failure to provide any of the aboveInfo.plistKey (NSSpeechRecognitionUsageDescriptionorNSMicrophoneUsageDescription) can cause the app to fail without warning when trying to access the voice recognition or microphone for live audio.

(Video) Xamarin Speech iOS

Request Authorization

To request the necessary user authorization that allows the app to access speech recognition, edit the main view controller class and add the following code:

using System;using UIKit;using Speech;namespace MonkeyTalk{ public subclass ViewController : UIViewController { protected ViewController (IntPtr handle) : base (handle) { // Note: this .ctor file should not contain any initialization logic. } public override void ViewDidLoad () { base.ViewDidLoad (); // Request user authorization SFSpeechRecognizer.RequestAuthorization ((SFSpeechRecognizerAuthorizationStatus status) => { // Take action based on status change (status) { case SFSpeechRecognizerAuthorizationStatus.Authorized: // User authorized speech recognition ... break; case SFSpeechRecognizerAuthorizationStatus.Denied: // User rejected speech recognition... break; case SFSpeechRecognizerAuthorizationStatus.NotDetermined: // Waiting for approval... break; case SFSpeechRecognizerAuthorizationStatus.Restricted: // The device is not authorized... break; } }); } }}

TheRequest Authorizationmethod ofSFSpeechRecognizerThe class requests permission from the user to access speech recognition using the reason given by the developer in theNSSpeechRecognitionUsageDescriptionkey theInfo.plistFile.

ASFSpeechRecognizerAuthorizationStatusResult is returned toRequest AuthorizationMethod callback routine that can be used to take action based on the user's permission.


Apple suggests waiting until the user has started an action in the app that requires voice recognition before requesting that permission.

Recognize recorded speech

If the app wants to recognize speech from a pre-recorded WAV or MP3 file, it can use the following code:

Using System;Using UIKit;Using Speech;Using Foundation;...public void RecognizeFile(NSUrl url){ // Access new recognizer var Recognizer = new SFSpeechRecognizer(); // Is the default language supported? if (recognizer == null) { // no, back to the caller return; } // Is detection available? if (!recognizer.Available) { // no, back to the caller return; } // Create recognition task and start recognition var request = new SFSpeechUrlRecognitionRequest (url); detectr.GetRecognitionTask (request, (SFSpeechRecognitionResult result, NSError err) => { // Was there an error? if (err != null) { // Handle error... } else { // Is this the final translation? if (result.Final) { Console.WriteLine("You said \"{0}\".", result.BestTranscription.FormattedString); } } });}

If you look at this code in detail, it first tries to create a speech recognizer (SFSpeechRecognizer). If the default language for speech recognition is not supported,Nullreturned and the function exited.

If speech recognition is available for the default language, the app will check if it is currently available for recognition usingAccessibleProperty. For example, detection may not be available if the device does not have an active internet connection.

ASFSpeechUrlRecognitionRequestarises from theNSUrlLocation of the recorded file on the iOS device and is passed to the speech recognition to process it with a callback routine.

When the callback is called when theNS erroris notNullAn error has occurred that needs to be handled. Because speech recognition is incremental, the callback routine can be called more than onceSFSpeechRecognitionResult.Finalproperty is tested to see if the translation is complete and the best version of the translation is written out (Best Transcription).

Detect Live Speech

If the app wants to recognize live speech, the process is very similar to recognizing recorded speech. For example:

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using System;using UIKit;using Speech;using Foundation;using AVFoundation;...#region Private Variablesprivate AVAudioEngine AudioEngine = new AVAudioEngine ();private SFSpeechRecognizer SpeechRecognizer = new SFSpeechRecognizer ();private SFSpeechAudioBufferRecognitionRequest LiveSpeechRequest = new SFSpeechAudioBufferRecognitionRequest ();private SFSpeechRecognitionTask RecognitionTask;#endregion...public void StartRecording (){ // Establish audio session var node = AudioEngine.InputNode; var recordingFormat = node.GetBusOutputFormat(0); node.InstallTapOnBus(0, 1024, recordingFormat, (AVAudioPcmBuffer buffer, AVAudioTime when) => { // Append buffer to recognition request LiveSpeechRequest.Append (buffer); }); // Start recording AudioEngine.Prepare(); NSError error; AudioEngine.StartAndReturnError(Output Error); // Has recording started? if (error != null) { // handle error and return ... return; } // Start recognition RecognitionTask = SpeechRecognizer.GetRecognitionTask (LiveSpeechRequest, (SFSpeechRecognitionResult result, NSError err) => { // Was there an error? if (err != null) { // Handle error ... } else { // If (result.Final) { Console.WriteLine ("You said \"{0}\".", result.BestTranscription.FormattedString); } } });}public void StopRecording (){ AudioEngine .To stop (); LiveSpeechRequest.EndAudio();}public void CancelRecording(){ AudioEngine.Stop(); RecognitionTask.Cancel();}

Looking at this code in detail, several private variables are created to handle the detection process:

private AVAudioEngine AudioEngine = new AVAudioEngine ();private SFSpeechRecognizer SpeechRecognizer = new SFSpeechRecognizer ();private SFSpeechAudioBufferRecognitionRequest LiveSpeechRequest = new SFSpeechAudioBufferRecognitionRequest ();private SFSpeechRecognitionTask RecognitionTask;

It uses AV Foundation to record audio that is routed to aSFSpeechAudioBufferRecognitionRequestto process the application for recognition:

var node = AudioEngine.InputNode;var recordingFormat = node.GetBusOutputFormat (0);node.InstallTapOnBus (0, 1024, recordingFormat, (AVAudioPcmBuffer buffer, AVAudioTime when) => { // Append buffer to recognition request LiveSpeechRequest.Append (buffer) ; });

The app will try to start recording and all errors will be handled if the recording fails to start:

AudioEngine.Prepare();NSError error;AudioEngine.StartAndReturnError (out error); // Has recording started? if (error != null) { // Handle error and return... return;}

The discovery task is started and a handle is kept to the discovery task (SFSpeechRecognitionTask):

RecognitionTask = SpeechRecognizer.GetRecognitionTask (LiveSpeechRequest, (SFSpeechRecognitionResult result, NSError err) => { ...});

The callback is used in a manner similar to that for pre-recorded speech above.

If the user stops the recording, both the audio engine and the speech recognition request are informed:

AudioEngine.Stop ();LiveSpeechRequest.EndAudio ();

If the user cancels the recognition, the audio engine and the recognition task are informed:

AudioEngine.Stop ();RecognitionTask.Cancel ();

Calling is importantRecognitionTask.Cancelwhen the user cancels the translation to free up both memory and the device's processor.


Missing provision ofNSSpeechRecognitionUsageDescriptionorNSMicrophoneUsageDescription Info.plistButtons can cause the app to fail without warning when trying to access voice recognition or the microphone for live audio (var node = AudioEngine.InputNode;). Please take a look...Providing a Description of Usesection above for more information.

Limits of Speech Recognition

Apple imposes the following limitations when working with speech recognition in an iOS app:

(Video) Text-to-Speech (Xamarin.Essentials API of the Week)

  • Speech recognition is free for all apps, but its use is not unlimited:
    • Individual iOS devices have a limited number of detections that can be performed per day.
    • Apps are throttled globally on a request-per-day basis.
  • The app must be prepared to deal with speech recognition network connection and usage rate limiting errors.
  • Speech recognition can cause high costs on the user's iOS device due to both battery drain and high network traffic. Because of this, Apple imposes a strict audio duration limit of about a minute or less of speech.

When an app routinely hits its rate throttling limits, Apple asks the developer to get in touch with them.

Privacy and usability considerations

Apple has the following proposal to be transparent and respect user privacy when integrating speech recognition into an iOS app:

  • If you record the user's speech, ensure that the app's UI clearly states the recording. For example, the app could play a "recording" sound and display a recording indicator.
  • Do not use speech recognition for sensitive user information such as passwords, health records, or financial information.
  • View the recognition resultsBeforeaffect them. This not only gives feedback on what the app is doing, but allows the user to handle detection errors as they are made.


This article introduced the new Speech API and showed how to implement it in a Xamarin.iOS app to support continuous speech recognition and transcribe speech (from live or recorded audio streams) to text.

  • SpeakToMe (example)


How can I improve Microsoft speech recognition? ›

If you want to retrain your computer to recognize your voice, press the Windows logo key, type Control Panel, and select Control Panel in the list of results. In Control Panel, select Ease of Access > Speech Recognition > Train your computer to better understand you.

How can I improve my voice recognition accuracy? ›

How to Improve Speech-to-Text Accuracy
  1. Add Custom Words. Every Speech-to-Text engine understands a large but limited set of words known as the Lexicon . ...
  2. Boost Phrases. Some phrases are frequent in a given specialized domain but otherwise are rare. ...
  3. Language Model Adaptation. ...
  4. Acoustic Model Adaptation.
Oct 17, 2022

How do I improve Apple speech recognition? ›

Dictating on the iPhone – how to get the best results
  1. Isolate your voice from background sounds. ...
  2. Enunciate clearly. ...
  3. Pick easily understood names for HomeKit. ...
  4. Dictate first, correct second. ...
  5. Don't bother with auto-punctuation for now! ...
  6. Share your own tips for dictating on the iPhone.
Sep 14, 2022

Is speech recognition software accurate? ›

Speech recognition accuracy rates are 90% to 95%. Here's a basic breakdown of how speech recognition works: A microphone translates the vibrations of a person's voice into an electrical signal. A computer or similar system converts that signal into a digital signal.

How accurate is IOS speech recognition? ›

Apple voice recognition - overview

Accuracy varies depending on your accent, how fast you speak, surrounding noise levels and the subject nature (it may struggle with technical words or people's names). Over 90% accuracy is common but you will need to edit the mistaken words using the keyboard.

Why is speech recognition difficult? ›

Automatic children speech recognition is always challenging due to limited corpus and varying acoustic features. One among those is zero speech corpus and large acoustic variability which limits the power of learning of training dataset.

How do you solve speech recognition problems? ›

These factors are often unique to a use case or a particular business need and include:
  1. Background noise.
  2. Punctuation placement.
  3. Capitalization.
  4. Correct formatting.
  5. Timing of words.
  6. Domain-specific terminology.
  7. Speaker identification.
May 20, 2021

What are some problems with voice recognition? ›

With the growing popularity of voice recognition technology comes a few problems.
5 Things That Can Interfere With Your Voice Recognition Software
  • Voices In The Background. ...
  • Speedy Talking, Dialects and More. ...
  • Music or Loud Noises in The Background. ...
  • A Speaker's Distance From The Microphone. ...
  • Similar-Sounding Words.
Jul 18, 2021

Can you beat voice recognition? ›

Vocal disguises and impersonations may fool voice recognition authentication. Research indicates that impersonating a voice can fool voice recognition authentication systems.

What is the best speech to text software? ›

Those programs that ranked highest on our criteria, generally offering a mix of robust features and wide platform availability, made our short list for testing:
  • Apple Dictation (macOS, iOS, iPadOS)
  • Apple Voice Control (macOS, iOS, iPadOS)
  • Google Assistant on Gboard.
  • Google Docs Voice Typing.
  • Microsoft Word Dictate.
Feb 3, 2023

How do I improve text to speech? ›

However, for some people, using text-to-speech software is still very difficult.
3 Ways to Improve Website Text to Speech
  1. Always Include Alt Text. What is alt text? ...
  2. Always Use Punctuation. You may be surprised to learn how much punctuation changes how a screen reader vocalizes your text. ...
  3. Include a 'Listen' Feature.

Which algorithm is best for speech recognition? ›

Traditional ASR algorithms

Hidden Markov models (HMM) and dynamic time warping (DTW) are two such examples of traditional statistical techniques for performing speech recognition.

What are the disadvantages of speech recognition? ›

Speech recognition software may not be able to transliterate the words of those who speak quickly, run words together or have an accent. It also drops in accuracy when more than one speaker is present and being recorded.

What is the most accurate voice recognition? ›

  • Comparison of the Best Speech Recognition Software.
  • #1) Dragon Professional.
  • #2) Dragon Anywhere.
  • #3) Google Now.
  • #4) Google Cloud Speech API.
  • #5) Google Docs Voice Typing.
  • #6) Siri.
  • #7) Amazon Lex.
Feb 12, 2023

Is speech recognition faster than typing? ›

In both cases, dictation is faster than typing. Speech recognition software can transcribe over 150 words per minute (WPM), while the average doctor types around 30 WPM. Professional transcriptionists type around 50-80 WPM, which is also much faster than physicians.

Which network is best for speech recognition? ›

Neural networks are very powerful for recognition of speech. There are various networks for this process. RNN, LSTM, Deep Neural network and hybrid HMM-LSTM are used for speech recognition.

Will speech recognition software replace typing? ›

Voice Recognition Software Finally Beats Humans At Typing, Study Finds : All Tech Considered In a face-off between voice entry and typing on a mobile device, voice recognition software performed significantly better. The results held true in both English and Mandarin Chinese.

What is the main problem in speech processing? ›

Background noise is one of the biggest challenges. Especially as voice recognition software leaves the confines of the personal computer to inhabit smart devices in varied environments, we need to deal with cross-talk, white noise, and other signal muddying effects. Jargon is another common reason for inaccuracy.

What is the most challenging type of speech? ›

The Most Difficult Speech: The Eulogy.

What are the three steps of speech recognition? ›

Speech recognition involves three processes: extraction of acoustic indices from the speech signal, estimation of the probability that the observed index string was caused by a hypothesized utterance segment, and determination of the recognized utterance via a search among hypothesized alternatives.

Which of the following is a limitation of voice recognition? ›

One of the major drawbacks of Speech Recognition technology in Personal Computers is the lack of accuracy and misinterpretation.

What is the challenge of speech recognition? ›

  • The challenge of accuracy. The accuracy of a Speech Recognition System (SRS) must be high to create any value. ...
  • The challenge of language, accent, and dialect coverage. ...
  • The challenge of data privacy and security. ...
  • The challenge of cost and deployment.
Aug 8, 2022

Does voice recognition hold up in court? ›

The spectrogram of an unidentified speaker is compared with that of an identified speaker in order to find similar patterns. The majority of the courts which have considered the question have ruled that voiceprint evidence is admissible.

What is the difference between text to speech and speech recognition software? ›

Text to speech is different from speech to text. Speech to text is a powerful speech to text application that can recognize and translate spoken language into text through computational linguistics. It is also called speech recognition or computer speech recognition.

Which algorithm is used for text to speech? ›

Text-to- speech synthesizer (TTS) is the technology which lets computer speak to you. The TTS system gets the text as the input and then a computer algorithm which called TTS engine analyses the text, pre-processes the text and synthesizes the speech with some mathematical models.

How do you use dictation effectively? ›

  1. Be specific. If you are passing on your dictation to a transcriptionist, any dictation you do needs to be done from a foundation that the person typing for you does not know what you know. ...
  2. Include punctuation. Many people question whether they should dictate punctuation. ...
  3. Practice makes perfect.
Sep 4, 2022

Does iPhone dictation have a time limit? ›

When you dictate in a search field, your dictated text may be sent to the search provider to process the search. When you use on-device Dictation, you can dictate text of any length without a timeout. You can stop Dictation manually, or it stops automatically when you stop speaking for 30 seconds.

Is dictation good for language learning? ›

Dictation can help develop all four language skills in an integrative way. Dictation helps to develop short-term memory. Students practice retaining meaningful phrases or whole sentences before writing them down. Dictation can serve as an excellent review exercise.

How do I calibrate Text-to-Speech? ›

Update text-to-speech settings
  1. Open your device Settings .
  2. Select Accessibility. Text-to-speech output.
  3. Choose your preferred engine, language, speech rate, and pitch. The default text-to-speech engine choices vary by device.

Can you speed up Text-to-Speech? ›

Step 2: Under the “App Settings” section on the left side of the screen, click “Accessibility.” Step 3: Scroll down to the “Text-To-Speech Rate” section and drag the slider left or right to make the text-to-speech speed slower or faster, respectively.

What machine learning algorithms for speech recognition? ›

Which Algorithm is Used in Speech Recognition? The algorithms used in this form of technology include PLP features, Viterbi search, deep neural networks, discrimination training, WFST framework, etc.

What is the difference between NLP and speech recognition? ›

NLP and Voice Recognition are complementary but different. Voice Recognition focuses on processing voice data to convert it into a structured form such as text. NLP focuses on understanding the meaning by processing text input. Voice Recognition can work without NLP , but NLP cannot directly process audio inputs.

Can we use CNN for speech recognition? ›

The CNN has three key properties: locality, weight sharing, and pooling. Each one of them has the potential to improve speech recognition performance.

What are the major challenges in speech recognition systems? ›

  • The challenge of accuracy. The accuracy of a Speech Recognition System (SRS) must be high to create any value. ...
  • The challenge of language, accent, and dialect coverage. ...
  • The challenge of data privacy and security. ...
  • The challenge of cost and deployment.
Aug 8, 2022

How can I improve my speech voice clarity? ›

Here are six tips to help you speak more clearly:
  1. Take deep breaths. Breathing is essential to producing a stronger voice and speaking. ...
  2. Adopt a good posture. A bad posture will affect not only your musculoskeletal system but also your speech clarity. ...
  3. Use a mirror. ...
  4. Swallow excess saliva. ...
  5. Watch your pitch. ...
  6. Speak slowly.
Nov 22, 2021

What are the three types of speech recognition? ›

There are two types of speech recognition. One is called speaker–dependent and the other is speaker–independent. Speaker–dependent software is commonly used for dictation software, while speaker–independent software is more commonly found in telephone applications.

What are the two factors of speech recognition program? ›

To meet these requirements, speech recognition systems use two types of models:
  • Acoustic models. These represent the relationship between linguistic units of speech and audio signals.
  • Language models. Here, sounds are matched with word sequences to distinguish between words that sound similar.

Can speech problems be fixed? ›

Fortunately, there are a number of ways that speech disorders can be treated, and in many cases, cured. Health professionals in fields including speech-language pathology and audiology can work with patients to overcome communication disorders, and individuals and families can learn techniques to help.

Which algorithm is used in speech recognition? ›

Traditional ASR algorithms

Hidden Markov models (HMM) and dynamic time warping (DTW) are two such examples of traditional statistical techniques for performing speech recognition.

Which are the key features for effective speech recognition? ›

According to the structure of the speech recognition system, a complete speech recognition system includes a feature extraction algorithm, acoustic model, and language model and search algorithm. The speech recognition system is essentially a multidimensional pattern recognition system.

What are the four different ways to perform speaker recognition? ›

Speaker recognition is a pattern recognition problem. The various technologies used to process and store voice prints include frequency estimation, hidden Markov models, Gaussian mixture models, pattern matching algorithms, neural networks, matrix representation, vector quantization and decision trees.


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