Large Language Models Mimic Human Speech Patterns with Increasing Accuracy.

Large language models accurately predict characteristics of natural speech – specifically speech reductions and prosodic prominence – following fine-tuning. Performance improves when models are trained on spoken, rather than written, data, suggesting speech corpora offer valuable benchmarks for evaluating and refining these artificial intelligence systems.

The capacity of large language models (LLMs) to generate human-like text necessitates rigorous evaluation beyond simple task completion. Researchers are increasingly focused on assessing whether these models exhibit cognitive plausibility – that is, whether their internal processes align with observed human behaviour. A study published recently details an approach utilising characteristics of natural speech – specifically speech reductions and prosodic prominence (the emphasis given to certain syllables) – as benchmarks for LLM performance. Sheng-Fu Wang (Academia Sinica), Laurent Prévot (CNRS & MEAE), and Jou-An Chi, Ri-Sheng Huang, and Shu-Kai Hsieh (National Taiwan University) present their findings in “Spontaneous Speech Variables for Evaluating LLMs Cognitive Plausibility”, demonstrating that models trained on spoken language data exhibit a greater capacity to predict these speech characteristics than those trained solely on written text. This work contributes to the development of more nuanced and behaviourally relevant evaluation metrics for artificial intelligence.

Language Models Accurately Predict Speech Phenomena of Reduction and Prominence

This study investigates the ability of large language models (LLMs) to predict reduction and prominence – key phonetic features of natural spoken language – across English, French, and Mandarin. Researchers extracted these production variables from spontaneous speech corpora and assessed model performance following training on diverse datasets – written text, spoken language, and mixed genres. Results demonstrate that, after fine-tuning, models outperform baseline performance in predicting both reduction and prominence, establishing the utility of high-quality speech corpora as valuable benchmarks for LLM evaluation. By focusing on production variables derived from spontaneous speech, the study moves beyond traditional linguistic benchmarks and offers a more nuanced assessment of model capabilities.

Reduction refers to the weakening or omission of sounds in connected speech (e.g., ‘going to’ becoming ‘gonna’). Prominence, conversely, relates to the increased perceptual salience of certain syllables or words, often achieved through increased duration, intensity, or pitch. These features are crucial for natural-sounding speech and effective communication.

Analysis reveals a clear advantage for models trained on spoken data when predicting both reduction and prominence, suggesting that exposure to the characteristics of spoken language enhances a model’s ability to represent and predict natural speech patterns. The observed performance indicates that models learn to capture aspects of speech production beyond simply processing linguistic content, offering valuable insights into their cognitive capabilities. Researchers pinpointed specific words and phrases consistently over- or under-predicted by the models, providing a nuanced understanding of their strengths and limitations.

The study establishes that training models on spoken language data yields more accurate predictions than utilising written text alone, underscoring the importance of incorporating speech corpora into the training process. This highlights the necessity of capturing the nuances of spoken language to enhance a model’s understanding of production variables. Figures detailing token frequency against predicted and actual labels further illuminate the relationship between these factors, demonstrating how model performance fluctuates with the frequency of the analysed tokens. The data indicates that models generally achieve higher accuracy with more common words, while performance diminishes with rarer terms, suggesting that the statistical properties of training data significantly influence a model’s ability to generalise to less frequent linguistic elements.

This approach, utilising high-quality speech corpora as benchmarks for LLMs, contributes to a broader effort to evaluate these models in a manner that aligns with human cognitive processes. Researchers propose future work to explore the impact of different training methodologies and model architectures on the prediction of speech phenomena, suggesting that further investigation could lead to even more accurate and robust models. They also highlight the potential for applying these models to a variety of real-world applications, such as speech recognition, speech synthesis, and language tutoring, demonstrating the broad applicability of this research. This study provides valuable insights into the capabilities of LLMs and paves the way for future advancements in the field of speech processing and natural language understanding.