Defining Acoustic vs. Linguistic Features

Speech is a complex signal that carries multiple layers of information. To understand and analyse it effectively, it’s essential to distinguish between its acoustic and linguistic properties.

What Are Acoustic Speech Features?

Acoustic speech features describe the physical properties of the sound wave produced during speech. These features can be observed and measured directly from the audio signal using signal processing techniques. They are independent of the meaning or language being spoken and are often used in tasks that require an understanding of how something is said rather than what is said.

Some of the most common acoustic features include:

• Pitch (Fundamental Frequency, F0): Reflects the intonation or melody of speech. Variations in pitch can indicate different emotions, question versus statement forms, or speaker characteristics.
• Formants (F1, F2, etc.): Resonant frequencies of the vocal tract that define vowel identity. They are important in phonetic and speaker analysis.
• Energy (Amplitude): Measures the loudness of the speech signal, which can reflect emphasis or emotional intensity.
• Spectral Features: Include spectral centroid, spectral flux, and Mel-frequency cepstral coefficients (MFCCs), which model the frequency content of the speech signal in ways that align with human perception.
• Duration and Speaking Rate: Help identify speech fluency, hesitation, and tempo. Slower or faster rates can indicate emotion or cognitive load.
• Voice Quality Measures: Such as jitter (frequency variation), shimmer (amplitude variation), and harmonic-to-noise ratio, which can indicate vocal strain or stress.

These features are particularly important in applications like speaker recognition, emotion detection, speech pathology, and quality assessment. They form a bridge between raw signal data and interpretable speech events.

What Are Linguistic Features in Audio?

Linguistic features represent the language-based aspects of speech. They are concerned with the content and structure of what is being said rather than the way it is delivered. Extracting linguistic features usually requires converting the speech signal into text using ASR or manual transcription, after which natural language processing (NLP) techniques can be applied.

Types of linguistic features include:

• Phonemes: The smallest units of sound that distinguish words in a language (e.g. /b/, /p/).
• Morphemes: The smallest meaningful units of language, such as roots, prefixes, and suffixes.
• Lexical Items (Words): The actual vocabulary used in an utterance.
• Syntax: The grammatical structure of sentences, including part-of-speech tags and phrase boundaries.
• Semantics: The meaning conveyed by the sentence, often modelled using embeddings or logical forms.
• Pragmatics: The interpretation of language in context, considering social cues, speaker intent, or conversational history.

Linguistic features are central to understanding and generating language. They are used in tasks like intent classification, machine translation, summarisation, information retrieval, and intelligent virtual assistants.

Key Distinction

To summarise, acoustic speech features relate to how something is said (e.g. tone, volume, pitch), whereas linguistic features relate to what is being said (e.g. vocabulary, grammar, meaning). Both are vital but serve distinct roles in speech analysis systems.

Extraction Techniques

The extraction of speech features is a foundational step in developing speech processing models. The techniques used depend on whether the focus is on acoustic or linguistic content.

Acoustic Feature Extraction

Acoustic features are derived from the raw audio waveform using signal processing algorithms. These processes typically segment the audio into small overlapping windows (e.g. 20–30 milliseconds) to capture variations over time.

Key techniques and tools include:

• Short-Time Fourier Transform (STFT): Breaks the audio into time-localised frequency bins, producing a spectrogram representation.
• Mel-frequency Cepstral Coefficients (MFCCs): Captures the short-term power spectrum of sound using a perceptually motivated Mel scale. MFCCs are standard in speech and speaker recognition.
• Linear Predictive Coding (LPC): Models the speech signal based on the assumption that it can be predicted from past samples. Used to estimate vocal tract characteristics.
• Pitch Tracking Algorithms: Such as autocorrelation or harmonic product spectrum, used to extract the fundamental frequency.
• Energy Envelope Calculation: Measures the amplitude variation across frames.
• Voice Quality Analysis Tools: Calculate jitter, shimmer, and other prosodic attributes.

Popular toolkits:

• Praat: Widely used for phonetic research and acoustic analysis.
• OpenSMILE: Designed for large-scale speech analysis and emotion detection.
• Librosa: A Python library for audio and music signal processing.
• Kaldi: A powerful toolkit used for speech recognition pipelines that includes acoustic modelling.

These features are often normalised and scaled before use in machine learning models to ensure consistency across recordings and speakers.

Linguistic Feature Extraction

Linguistic features, unlike acoustic ones, are usually obtained after some level of language understanding has been applied to the speech. This typically involves:

1. Automatic Speech Recognition (ASR): Converts speech into text.
2. Forced Alignment: Aligns audio with phonetic transcriptions to identify phoneme boundaries.
3. Natural Language Processing (NLP): Extracts structural and semantic features from the resulting text.

Common linguistic feature extraction methods:

• Tokenisation and Part-of-Speech (POS) Tagging: Breaks text into words and assigns grammatical categories.
• Syntactic Parsing: Identifies sentence structure, including subjects, objects, and predicates.
• Named Entity Recognition (NER): Identifies and classifies proper nouns such as people, places, and organisations.
• Word Embeddings: Models such as Word2Vec, GloVe, or contextual embeddings like BERT, capture meaning based on word co-occurrence and context.
• Dialogue Act Tagging: Labels conversational turns based on intent (e.g. question, affirmation, command).

Toolkits include:

• spaCy and NLTK (Python): Comprehensive libraries for text preprocessing and syntactic analysis.
• ELAN: A tool for time-aligned transcription and annotation of linguistic and gestural data.
• Speechmatics, Whisper, Kaldi: ASR tools to transcribe audio as a basis for further linguistic analysis.

In multilingual scenarios, language identification tools may be used beforehand to route the signal to the correct linguistic model.

Use in Different Model Types

The distinction between acoustic and linguistic features plays a critical role in choosing the appropriate architecture for speech and language applications.

Acoustic Feature Use Cases

Acoustic features are highly effective in scenarios where understanding the style or source of speech is important, rather than the exact content.

Common applications include:

• Speaker Recognition and Verification: Systems build acoustic profiles from voice features like MFCCs and pitch contours to uniquely identify individuals.
• Emotion Detection: Models use features such as pitch range, energy, and jitter to identify emotional states like anger, joy, or sadness.
• Speech Quality Assessment: Tools evaluate call quality or microphone performance by analysing acoustic integrity.
• Prosody Modelling: Captures rhythm, stress, and intonation patterns essential in natural sounding TTS systems.

These systems typically operate without relying on word-level understanding, making them robust to language mismatches or noisy environments.

Linguistic Feature Use Cases

Linguistic features are essential in applications that require comprehension, content interpretation, or generation.

These include:

• Voice Assistants and Chatbots: Require language understanding to interpret commands and respond accurately.
• Speech-to-Text Transcription: Converts spoken language into readable text with proper punctuation and structure.
• Machine Translation: Relies on syntax and semantics to convert speech from one language to another.
• Information Extraction: Gathers facts and named entities from speech data.
• Text-to-Speech (TTS): Converts structured language input into synthesised speech, often augmented with prosodic information.

Linguistic models are increasingly based on transformer architectures like BERT or GPT, which allow nuanced understanding of language beyond keywords or syntax.

Integrating Both

In hybrid models, acoustic and linguistic features are often combined to improve accuracy. For example, in emotion-aware voice assistants, acoustic features might detect frustration while linguistic features identify complaint patterns. Together, they allow systems to adapt responses more intelligently.

Feature Selection and Dimensionality Reduction

As both acoustic and linguistic analyses can produce hundreds or even thousands of features, managing this high-dimensional data becomes a technical priority. Too many features can overwhelm models, introduce noise, and reduce performance. Efficient feature selection and dimensionality reduction are therefore vital.

Techniques for Dimensionality Reduction

• Principal Component Analysis (PCA): A linear algebra technique that projects high-dimensional data into a lower-dimensional space while preserving variance. Commonly used for visualising and compressing acoustic features.
• Linear Discriminant Analysis (LDA): Projects data in a way that maximises class separability. Effective for tasks with labelled classes such as emotion or speaker categories.
• Autoencoders: Deep learning models that learn a compressed representation of input features. Suitable for unsupervised dimensionality reduction of both acoustic and linguistic data.
• Non-negative Matrix Factorisation (NMF): Especially useful when feature vectors must remain positive (e.g. spectrograms).

Techniques for Feature Selection

• Filter Methods: Use statistical metrics like correlation, chi-squared scores, or mutual information to rank features independently of the model.
• Wrapper Methods: Evaluate feature subsets using a specific model and score their performance (e.g. recursive feature elimination).
• Embedded Methods: Integrate feature selection into model training using regularisation techniques like LASSO or decision tree pruning.

The choice of method depends on the model type, data size, and the trade-off between interpretability and performance. Often, a mix of filter and embedded methods yields the best results.

Illustrative Examples of Combined Feature Models

Combining acoustic and linguistic features often yields a more comprehensive understanding of speech. Below are hypothetical examples that illustrate how such hybrid models can be used effectively in different domains.

Example 1: Detecting Frustration in Customer Service Calls

Imagine a voice analytics tool designed for call centres. The goal is to detect whether a caller is becoming frustrated.

• Acoustic Features Used: Rising pitch, increased speaking rate, louder volume, and tremor in voice signal.
• Linguistic Features Used: Frequent use of negative words (“problem”, “angry”), repetition of complaints, and use of rhetorical questions.

By combining these signals, the system can flag calls where the emotional tone doesn’t match the words – for instance, a caller sounding stressed even when using polite language. This insight enables agents to intervene or escalate the case proactively.

Example 2: Improving Speaker Attribution in Group Meetings

Suppose you’re developing a tool to transcribe business meetings and assign each statement to the correct speaker.

• Acoustic Features Used: Voiceprint vectors from MFCCs and pitch signatures.
• Linguistic Features Used: Speaker-specific vocabulary or recurring phrases (e.g. one participant frequently says “Let me clarify”).

In situations where multiple speakers overlap or audio quality is low, the system can cross-reference voice cues with known linguistic patterns to attribute speech more accurately.

Example 3: Enhancing Pronunciation Feedback in Language Learning Apps

A mobile app is being developed to help users learn correct pronunciation in a second language.

• Acoustic Features Used: Vowel duration, formant transitions, stress patterns.
• Linguistic Features Used: Phoneme sequence alignment, morphological accuracy, and expected word stress patterns.

Together, the app provides both sound-based feedback (“You’re not rounding the vowel enough”) and structure-based feedback (“You’re stressing the wrong syllable in this word”), resulting in more holistic learning.