If you use Siri, Alexa, Cortana, Amazon Echo, or others as a part of your daily lives, you would accept that Speech recognition has become a ubiquitous part of our lives. These artificial intelligence-powered voice assistants convert the verbal queries of users into text, interpret and understand what the user is saying to come up with an appropriate response.There is a need for quality data collection to develop reliable speech, recognition models. But, developing speech recognition software is not a simple task – precisely because transcribing human speech in all its complexity, such as the rhythm, accent, pitch, and clarity, is difficult. And, when you add emotions to this complex mix, it becomes a challenge.What is Speech Recognition?Speech recognition is software’s ability to recognize and process human speech into text. While the difference between voice recognition and speech recognition might seem subjective to many, there are some fundamental differences between the two.Although both speech and voice recognition form a part of the voice assistant technology, they perform two different functions. Speech recognition does automatic transcriptions of human speech and commands into text, while voice recognition only deals with recognizing the speaker’s voice.Types of Speech RecognitionBefore we jump into speech recognition types, let’s take a brief look at speech recognition data.Speech recognition data is a collection of human speech audio recordings and text transcription that help train machine learning systems for voice recognition.The audio recordings and transcriptions are entered into the ML system so that the algorithm can be trained to recognize the nuances of speech and understand its meaning.While there are many places where you can get free pre-packaged datasets, it is best to get customized datasets for your projects. You can select the collection size, audio and speaker requirements, and language by having a custom dataset.Speech Data SpectrumSpeech data spectrum identifies the quality and pitch of speech ranging from natural to unnatural.Scripted Speech recognition dataAs the name suggests, Scripted speech is a controlled form of data. The speakers record specific phrases from a prepared text. These are typically used for delivering commands, emphasizing how the word or phrase is said rather than what is being said.Scripted speech recognition can be used when developing a voice assistant that should pick up commands issued using varied speaker accents.Scenario-Based speech recognitionIn a scenario-based speech, the speaker is asked to imagine a particular scenario and issue a voice commanding based on the scenario. This way, the result is a collection of voice commands that are not scripted but controlled.Scenario-based speech data is required by developers looking to develop a device that understands everyday speech with its various nuances. For instance, asking for directions to go to the nearest Pizza Hut using a variety of questions.Natural Speech RecognitionRight at the end of the speech spectrum is speech that is spontaneous, natural, and not controlled in any manner. The speaker speaks freely using his natural conversational tone, language, pitch, and tenor.If you want to train an ML-based application on multi-speaker speech recognition, then an unscripted or conversational speech dataset is useful.Data Collection components for Speech Projects
A series of steps involved in speech data collection ensure that the collected data is of quality and help in training high-quality AI-based models.Understand required user responsesStart by understanding the required user responses for the model. To develop a speech recognition model, you should gather data that closely represent the content you need. Gather data from real-world interactions to understand user interactions and responses. If you are building an AI-based chat assistant, look at the chat logs, call recordings, chat dialog box responses to create a dataset.Scrutinize the domain-specific languageYou require both generic and domain-specific content for a speech recognition dataset. Once you have collected generic speech data, you should sift through the data and separate the generic from specific.For example, customers can call in to ask for an appointment to check for glaucoma in an eye care center. Asking for an appointment is a highly generic term, but glaucoma is domain-specific.Moreover, when training a speech recognition ML model, make sure you train it to identify phrases instead of individually recognized words.Record Human SpeechAfter gathering data from the previous two steps, the next step would involve getting humans to record the collected statements.It is essential to maintain an ideal length of the script. Asking people to read more than 15 minutes of text could be counterproductive. Maintain a minimum 2 – 3 second gap between each recorded statement.Allow the recording to be dynamicBuild a speech repository of various people, speaking accents, styles recorded under different circumstances, devices, and environments. If the majority of future users are going to use the landline, your speech collection database should have a significant representation that matches that requirement.