What Is Speech Recognition?

Speech recognition technology automatically converts recorded audio to digital text. Traditionally, a human would listen to the audio file and type it into a text file to repurpose spoken content for different media. But now, speech recognition technology uses artificial intelligence to convert speech to text with improved accuracy and speed. It is separate from voice recognition, which classifies audio recordings by their speaker. Speech recognition increases digital accessibility, application efficiency, and business productivity.

There are several usecases of speech recognition software.

Virtual assistants

An everyday use case of speech recognition technology is in virtual assistants like Alexa. Users can speak voice commands, with speech recognition software converting these into actions that a virtual assistant uses. You can leverage this to ask the assistant to play music, make a reminder, call someone, or search the web for information.

Speech recognition tools are also helpful for those with physical impairments. They allow people to control computer interfaces with their voice. As speech recognition software converts speech into text, it can trigger an action on the computer device.

Transcription

Businesses and individuals can use speech recognition programs to transcribe recordings, lectures, demonstrations, and interviews automatically. By converting speech into text, individuals can dictate emails or even generate transcriptions of conversations, saving time and money.

Speech recognition technology can also document what people say in meetings. This approach saves your business from having to assign someone to take notes, saving time and boosting the productivity of employees.

Audio content moderation

Businesses can use speech recognition applications to monitor spoken words and word sequences for inappropriate language. Automatic speech recognition can understand audio and voice signals to moderate and censor toxic content in live streams or flag content with a content warning. The approach is useful for automating moderation and enhancing user safety when online.

Live translation

Translation platforms use speech recognition technologies to automatically record and transcribe conversations. The tools capture, translate, and then use text-to-speech to read out what was said in another language. Users can have a live conversation with a speaker from another language.

Business analytics

Businesses can use speech recognition systems to turn customer phone calls into transcripts. They can then extract actionable insights from these conversations, using them to understand more about customer sentiment or the context of a call. Companies can use the insights to improve customer engagement and enhance their support teams.

Enhance documentation

Health, finance, science, law and other specialized fields frequently uses industry-specific jargon that is long and time-consuming to note down. Specialists can use speech-to-text software to transform their speech input into written documents. Speech recognition can understand and process domain specific language when trained in specific terminology.

Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language. It is a core process used by speech recognition software to convert speech to text. NLP helps these systems convert sounds into accurate predictions about what words those sounds may mean.

Disambiguation

The conversion process from speech to text introduces ambiguities due to homophones (words that sound the same but have different meanings) or unclear pronunciation. NLP helps address these challenges by providing context. It uses statistical models that predict the likelihood of a sequence of words occurring together. This prediction is based on the frequency and patterns of words as they appear in the training data. That way it can accurately identify words most likely to be correct in the given context.

Complex language features

Human language has several features like sarcasm, metaphors, variations in sentence structure, plus grammar and usage exceptions that take humans years to learn. Programmers use machine learning methods to train NLP applications to recognize and accurately understand these features from the start. This helps in understanding speech that may be grammatically complex or contains idiomatic expressions.

Intent recognition

NLP algorithms can identify commands, questions, or other intentions and then generate appropriate responses or actions, making the interaction more natural and effective. This aspect is especially important in voice-activated assistants and other interactive systems where the user expects not just an acknowledgment of their speech but a relevant response or action.

Continuous learning

NLP systems can learn from new data and improve over time. As these systems are exposed to more speech patterns, accents, dialects, and languages, they adapt their models to better understand and process speech. They can adapt to individual users’ speech patterns and personalize the speech recognition experience.

Speech recognition systems use a microphone to collect audio sounds and conversation signals. They process it in stages as follows.

Cleaning

The software cleans the raw audio, modulating it to regulate pitch, volume, and tempo. At this stage, computer speech recognition removes any background noise irrelevant to the recording.

Phoneme identification

Once you have this clean audio file, speech recognition technology breaks down the sounds in the recordings into individual phonemes. Phonemes are the smallest unit of sound in a language. Each phoneme correlates to a mathematical representation, allowing the speech recognition software to determine what people say in the audio.

Transformer based speech recognition technology use a multi-layer convolutional network (CNN) as a feature extractor, which takes an input audio signal and outputs audio representations, also considered as features. They are fed into a transformer network to generate contextualized representations.

Natural language processing

The transformer architecture also yields very good model performance and results in various NLP tasks. Training NLP algorithms with large data samples increases the algorithms’ accuracy. Domain specific data samples are used to ensure the NLP makes more accurate predictions about industry-specific jargon and phrases. Using NLP, the software joins phonemes in highly likely sequences to form words.

The speech recognition process uses various algorithms to function correctly. Over time, the prominence of these different algorithms is changing. Hidden Markov models and dynamic time warping are increasingly being replaced by RNNs and speaker diarization.

Hidden Markov models

Hidden Markov models (HMMs) are a traditional algorithm used in speech recognition software to manage variance in speech patterns. Different accents and dialects cause people to pronounce words differently, with HMMs handling these potential discrepancies. As more training data with accent variations becomes available, HMM has become more effective at voice recognition and less important in speech recognition.

Dynamic time warping

Dynamic time warping (DTW) further aids HMM in enabling speech recognition software to understand sound. Two voices in a conversation may speak at various speeds and with distinct accents. DTW synchronizes the other voices, speeding them up or slowing them down to ensure they’re at the same speed.

When recordings are the same length and speed, speech recognition software finds it easier to accurately break down audio into sounds and convert those sounds into language assumptions.

Recurrent neural networks

Recurrent neural networks are a form of deep learning that improves upon more traditional speech recognition algorithms. RNNs consider previous sounds in predicting what may come next in human speech, enhancing their accuracy. The algorithm also effectively improves over time when understanding someone’s accent and dialect, making it useful for virtual assistants.

Speaker diarization

Speaker diarization is a technique that modern speech recognition systems use to understand voice changes and interpret who is speaking in a conversation. Beyond just recognizing speech, this strategy allows software recognition systems to differentiate between speakers in a recording.

Generative AI, while not a direct component of the speech recognition process, plays a complementary and increasingly significant role in speech recognition. Generative AI converts text from speech recognition output back into speech. It can re-generate spoken audio that closely mimics human speech patterns, including intonations, emotions, and accents. It can create more interactive and engaging user experiences for applications like virtual assistants, audiobooks, and language learning tools.

Generative AI models are capable of learning and synthesizing speech in multiple languages and dialects. They can also generate speech that matches a user's accent, making devices easier to understand and interact with. This personalization extends to creating unique voices for individuals who cannot speak.

Amazon Transcribe is a fully managed audio-to-text service that uses machine learning to transcribe quickly and accurately. Transcribe has features that you can use to enter audio input, produce easy-to-read transcripts, improve domain-specific accuracy with customization, and redact sensitive personal information to ensure customer privacy. With Amazon Transcribe, you can:

• Identify and extract critical business insights from conversations, video files, customer calls, and more.
• Enhance business outcomes by leveraging state-of-the-art speech recognition models.
• Obtain ML-powered insights and transcriptions with Transcribe Call Analytics, Transcribe Medical, and Transcribe Subtitling.

Get started with speech recognition systems on AWS by creating a free AWS account today.