Introduction

The ability to recognize and synthesize natural human speech is an astounding feat of modern artificial intelligence. Seamless voice interactions with machines like virtual assistants and customer service chatbots have become integral to our digital lives. But how exactly do AI systems listen to, comprehend and respond to human voices? In this in-depth post, we’ll explore the technical processes enabling machines to decode speech.

Capturing Speech Audio

The first step in speech recognition is microphone input to capture spoken words as analogue audio signals. Built-in smartphone mics or external mics sample the continuous sound pressure waves at over 40,000 times per second to produce discrete digital audio data.

Digital audio sampling converts an analogue audio signal into a stream of numbers that represent the audio waveform's amplitude over time. An analogue-to-digital converter measures the amplitude of the analogue signal at regular intervals, known as the sample rate, and converts each measurement to a binary number. Common sample rates are 44.1 kHz and 48 kHz. Higher sample rates capture more audio detail but require more digital storage space. Key factors in audio quality are the sample rate, the bit depth (number of bits per sample), and the linearity of the conversions between analogue and digital domains.

This digital audio representation can then be fed into machine learning models.

Recognising Speech

Next, the audio data is passed through specialized deep learning models trained to correlate audio patterns with the fundamental phonetic units of speech known as phonemes.

Phenomes are the basic units of sound in spoken language that distinguish one word from another. Specifically, phenomes are the smallest units in the sound system of a language that are capable of conveying a distinction in meaning. For example, the words "bat" and "pat" differ by just one phenome: the first consonant sounds /b/ and /p/. Languages use a limited set of phenomes, combined in different ways, to construct their vocabularies.

The models analyse the spectral features within each frame, like loudness patterns and frequency content. Deep learning techniques extract increasingly higher-level latent features, progressively refining the recognition capability. The models match extracted audio features to stored phoneme representations learned during training. Chains of recognized phonemes are combined to decode words and phrases.

Massive datasets of annotated speech samples provide the thousands of hours of labelled training data needed. Common techniques to improve performance include attention mechanisms focusing on salient audio, and language models predicting likely word sequences. Salient audio being the parts of a recorded sound that are most prominent, meaningful, or attention-grabbing to the listener.

Understanding Natural Language

The output text from the speech recognition stage is then processed by natural language understanding modules to comprehend the meaning and intent. This goes beyond transcribing speech to actually understanding linguistic context.

Approaches such as word embeddings represent text in the modelling and is where where relationships between words emerge, allowing semantic inference of concepts like intent, entities and keywords. Word embeddings are vector representations of words learned by AI models that capture semantic meaning allowing words with similar meanings to have similar embeddings, thereby enabling more accurate speech processing. Pretrained language models leverage transformers and self-attention to model language structure. The extracted meaning then guides the system’s response.

Responding Intelligently

To provide natural-sounding voiced responses, AI systems leverage text-to-speech synthesis. Input text is passed through deep learning models that directly generate raw speech waveforms, conditioned on prosodic features.

Prosodic refers to the rhythm, stress, and intonation of speech and includes:

• Prosody - The patterns of stress and intonation in a language.

• Rhythm - The regular recurrence of sound patterns in speech, like syllable timing and stress patterns.

• Stress - Emphasising certain words or syllables within words by increasing volume, pitch, and length. This conveys meaning.

• Intonation - Variations in pitch when speaking, which can convey emotion, emphasis, questions, etc.

• Pitch - Highness or lowness of voice pitch as pitch patterns provide cues in speech.

• Pauses - Brief stops in speech that indicate boundaries between word groups or phrases.

• Rate - The speed at which speech is spoken as rate affects understanding.

• Loudness - Variations in vocal intensity and amplitude as loudness stresses syllables.

In effect, prosodic features of speech include all the qualities that go beyond just the words themselves - elements like rhythm, tone, pauses, speed, and emphasis. Understanding prosody is a key element of natural language processing.

Neural vocoders then add fine-grained vocal detail to the waveforms using generative networks. Transfer learning from large speech corpora (large, structured collections of texts or speech data that are used for linguistic analysis and language modelling) produces diverse voices. AI can even mimic a specific speaker’s voice using just a few samples, thereby enabling personalisation.

The generated speech response is optimised to sound human through techniques like adding disfluencies like “um” and breathing sounds. The result is contextually relevant, naturally intonated dialogue.

Applications and Impacts

These speech AI capabilities enable numerous applications:

• Intelligent virtual assistants like Siri and Alexa

• Automated phone customer service agents

• Voice-controlled smart devices and wearables

• Speech transcription services like Otter.ai

• Tools to aid hearing and speaking impaired users

• More natural human-computer interaction

However, risks like data privacy violations, misuse of synthesized voices, and job automation also warrant ethical considerations and we have covered these in a number of our other blogs on our website.

Overall, sophisticated deep learning techniques have enabled transformative advances in machines’ ability to comprehend and respond to natural human speech. With sufficient data and compute power, AI is mastering abilities once considered exclusively human.

CONTACT INFORMATION
Need to Reach Out to Us?

BEFORE YOU GO
Tell Us What You Think