Explainability for Speech Models: On the Challenges of Acoustic Feature Selection
Authors: Dennis Fucci, Beatrice Savoldi, Marco Gaido, Matteo Negri, Mauro Cettolo, Luisa Bentivogli (Università di Trento, Fondazione Bruno Kessler, 2024)
Topic: Multimodal

Abstract
Spurred by the demand for transparency and interpretability in Artificial Intelligence (AI), the field of eXplainable AI (XAI) has experienced significant growth, marked by both theoretical reflections and technical advancements. While various XAI techniques, especially feature attribution methods, have been extensively explored across diverse tasks, their adaptation for the \textit{speech} modality is comparatively lagging behind. We argue that a key challenge in feature attribution for speech processing lies in identifying informative acoustic features. In this paper, we discuss the key challenges in selecting the features for speech explanations. Also, in light of existing research, we highlight current gaps and propose future avenues to enhance the depth and informativeness of explanations for speech.

Introduction
Spoken language, as perhaps our most natural form of interaction, is the foundational element of many technologies we interact with in our daily lives, from virtual assistants to voice dictation. More recently, the emergence of highly capable speech foundation models has facilitated and expanded the adoption of speech technologies on an unprecedented multilingual scale. In light of this proliferation, a need arises to prioritize transparency and interpretability, qualities already demanded in the growing landscape of Machine Learning (ML). As a response, the field of eXplainable AI (XAI) has risen prominently, with the aim of facilitating understanding of the rationale behind model decisions and fostering users' trust. XAI is also reinforced by the establishment of norms and legal frameworks, as seen in the European Union's General Data Protection Regulation, which enshrines the "right to explanation," and the AI Act, which emphasizes transparency as a pivotal component of ML applications. XAI encompasses various tasks and methods, such as identifying relevant model components for specific predictions, understanding the information processed by these components, and determining which input elements guide the model's predictions. The latter task is the focus of feature attribution methods, which provide intuitive explanations by visualizing which input elements (e.g., pixels in an image or words in a sentence) have influenced the model’s predictions. These methods assign a score to each input feature, quantifying its importance or contribution to the output: higher scores indicate greater importance of the corresponding input features for generating the output. They can help identify potential causes for errors and unexpected behaviors, as well as analyze the model’s response to specific input properties. Overall, these explainability methods serve to present the reason why models make specific predictions by establishing a connection between input and output as a form of intuitive explanation for humans, thereby enhancing interpretability. Despite numerous efforts to differentiate the closely related concepts of explainability and interpretability, no consensus exists in the literature on their definitions. In this paper, we adopt a perspective where explainability refers to the process of extracting insights from a model's workings through specific techniques, while interpretability refers to the understanding process of those insights, crucial to make them actionable. Over time, ongoing efforts have aimed to refine feature attribution techniques and provide more effective explanations. However, it is essential to recognize that the effectiveness of feature attribution explanations relies not only on the techniques themselves but also on the informativeness of the input features used as explanatory variables. If an explanation highlights unintelligible or poorly informative features, it does little to enhance the understanding of the model's behavior. This can undermine key principles in XAI, such as accuracy—the property of correctly reflecting the factors that led the model to a specific decision, including all relevant information—and meaningfulness—the property of offering explanations that are comprehensible to the user. In fields involving images or texts, feature representations are typically constrained to pixels and words, respectively. However, for speech, multiple input representations can be adopted, each emphasizing different acoustic aspects. Indeed, a sequence of speech elements not only conveys the meaning of what is said (like words in a text) but also bears a wealth of additional information useful for both human understanding and automatic processing (e.g., intonation, loudness, speaking rate). Consequently, when employing feature attribution methods, the resulting explanations can vary significantly in shape and focus on more or less informative characteristics depending on the type of speech representation used. To date, research on feature attribution for speech is notably limited to few applications, including classification and generative tasks, which offer a somewhat fragmented picture in the choice of speech representations, thus providing limited insights on the relation between the features considered and the explanations based upon them. In light of the above, this paper reflects on the impact of the chosen acoustic features in explaining the rationale behind speech models, aiming to gain a deeper understanding of the trade-offs associated with acoustic features. By first offering a gentle introduction to the rich and multidimensional nature of speech and its digital representation, we identify current gaps and potential avenues for effectively incorporating this multidimensionality into XAI for speech models. Our discussion will focus on two critical factors: i) the amount of information these features provide about the model's behavior, which influences the richness of the explanations, and ii) the level of detail of such information, which determines the granularity of the explanations. We will also explore how these aspects impact both the accuracy and meaningfulness of the explanations, ultimately shaping their overall interpretability.

Conclusion
This paper has examined the role of acoustic features and their selection for explaining speech models.More specifically, we considered a specific subfield of XAI, namely, feature attribution, which connects input features to outputs as a form of explanation. Previous research has not explicitly addressed how to incorporate features into the explanation process within the speech domain, where input is encoded in more varied ways compared to other fields, such as text.This has led to diverse approaches, each with different implications for what can and cannot be explained about model behavior, and with the risk of not fully or accurately representing the model's functioning. By discussing the key characteristics of speech and the properties of the most adopted acoustic features, we argue that explanations should ideally encompass all available dimensions, particularly time and frequency, as both are essential for a comprehensive understanding of the models' rationale. We have also discussed challenges associated with aligning explanations at high granularity with human understanding, emphasizing solutions that provide flexibility in the analysis, allowing for adjustments between more or less detail as needed.Building on these insights, our ongoing research focuses on developing feature attribution techniques that operate on spectrograms at the finest possible unit level, integrating both time and frequency dimensions. Our aim is to generate explanations that are accurate and meaningful for experts, as well as adaptable for non-expert users. More broadly, we hope that our reflections will be beneficial and thought-provoking for researchers currently working in, or entering, the field of XAI for speech models, thereby contributing to a deeper understanding of the rationale behind these models.