Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features (2024)

Abstract: Predictive models make mistakes and have biases. To combat both, we need to understand their predictions.Explainable AI (XAI) provides insights into models for vision, language, and tabular data. However, only a few approaches exist for speech classification models. Previous works focus on a selection of spoken language understanding (SLU) tasks, and most users find their explanations challenging to interpret.We propose a novel approach to explain speech classification models. It provides two types of insights. (i) Word-level. We measure the impact of each audio segment aligned with a word on the outcome. (ii) Paralinguistic. We evaluate how non-linguistic features (e.g., prosody and background noise) affect the outcome if perturbed.We validate our approach by explaining two state-of-the-art SLU models on two tasks in English and Italian. We test their plausibility with human subject ratings. Our results show that the explanations correctly represent the model’s inner workings and are plausible to humans.

## introduction as models increase in complexity, understanding how they work and the reasons behind their outputs becomes more challenging. however, this understanding is crucial for improving performance and addressing biases. various explainable ai (xai) techniques, such as gradient-based @xcite @xcite and input perturbation @xcite approaches, have been proposed to gain insights into computer vision model behavior. these techniques have been successfully applied in language @xcite @xcite and tabular @xcite @xcite models. despite significant progress in xai for vision, text, and structured data models, explanations for @xcite . audio aligned to words represented through color. bars show word-level attributions for target classes increase (green) and bedroom (orange). spoken language understanding (slu) models remain largely unexplored. some existing approaches provide spectrogram- @xcite or phoneme-based @xcite explanations. however, they are too fine-grained for broader speech tasks (e.g., intent detection or emotion recognition) where other factors interplay to convey meaning, e.g., acoustic features, linguistic aspects, and prosody. capturing such aspects requires tailor-made explainability solutions that are also easy for human actors to understand. our goal is to explain predictions by describing the interaction between input utterance components and model predictions. utterances incorporate semantic and paralinguistic information from the speaker's voice and external conditions, such as prosody and acoustics. following @xcite definition of explanation, we want to provide interpretable representations of utterances to help humans understand model behavior, addressing the following research questions (rqs). ## methodology to quantify the contribution of each utterance part to a prediction, we compute word-level attribution scores as follows. first, we align the audio signal to its transcript and get word-level timestamps. then, we use a perturbation-based technique to compute the contribution of each spoken word to the prediction by modifying the input and observing changes in the prediction. specifically, we propose a method based on the leave-one-out and local interpretable model-agnostic explanations (lime; @xcite techniques. we follow a similar perturbation-based approach to measure the contribution of paralinguistic aspects. given an input utterance, we perturb the raw audio signal and measure the effect on the model prediction. we consider pitch to account for prosody, and audio stretching, background noise, and reverb levels for channel-related aspects. we generate explanations by assigning a single numerical attribution score to each uttered word ( §2.1) and paralinguistic feature ( §2.2). each score is generated via input perturbation and quantifies the entity's contribution (either a word or a paralinguistic feature) in predicting a given target class. ## experimental setup datasets and tasks. we evaluate our method on three datasets and two tasks: fluent speech commands (fsc; @xcite and the italian intent classification dataset (italic; @xcite for the intent classification (ic) task, and iemocap @xcite for emotion recognition (er). fsc is a widely used benchmark dataset for the ic task. the dataset contains recordings of interactions with home voice assistants. the goal is to predict an intent as the combination of three independent predictions, i.e., an action (e.g., "increase"), an object (e.g., "heat"), and a location (e.g., "bedroom"). we focus on the test set, which comprises 3793 audio samples. italic is an ic dataset for the italian language, including 60 unique intents. the test set consists of 1441 samples. we use the "speaker" setup, wherein each speaker utterances belong to a single set among the train, validation, and test. iemocap is a dataset for the er task annotated with emotion labels (i.e., happiness, anger, sadness, frustration, and a neutral state). it consists of recorded interactions between pairs of actors engaged in scripted scenarios involving ten unique actors. among its five sessions, we consider session '1', consisting of 942 utterances. ## plausibility user study plausibility to humans is another essential desideratum of good explanations. it measures whether explanations are reasonable, believable, and, more generally, align with human reasoning @xcite . we conducted a user study to assess whether our approach produced plausible explanations. we focused on word-level audio segment attribution in the intent detection task in english and italian. the target of our study was practitioners knowledgeable in machine learning. see appendix d for full details. ## related work few works address interpretability by design for speech and audio, like the prototypical networks @xcite and attentionbased explanations @xcite . most approaches focus on post-hoc interpretability to explain (already trained) models. we categorize them based on the form of the provided explanations. multiple studies @xcite use layer-wise relevance propagation (lrp) @xcite , initially proposed for image classification, to explain audio analysis tasks. most works represent explanations as time-frequency heatmaps over spectrograms @xcite al. (2018); frommholz et al. (2023); colussi and ntalampiras (2021); arras et al. (2019) , inter alia), or heatmaps over ad-hoc terms @xcite . while experts are familiar with spectrograms, they are challenging for laypersons to interpret. @xcite also use lrp to derive relevance scores for individual samples of the input waveform. interpreting explanations as sets of individual signal samples lacks abstraction and disregards sample context. we propose a more user-friendly and intuitive approach to explanation. similarly, @xcite assign relevance scores to audio frames for asr, i.e., raw data bins of predefined size in the time dimension. soundlime @xcite applies lime @xcite to equal-width segments within the time, frequency, or timefrequency domains. however, the chosen segment size affects these temporal explanations. moreover, their explanations are not grounded in spoken words or paralinguistic information, limiting interpretability for semantic contexts like speech classification. @xcite is similar to our approach, as it tests fixed-width audio segments and audio segments aligned with phonemes. however, they require phoneme-level annotations, which limits evaluation to when such labeling is available. moreover, their method specializes in phoneme recognition. in contrast, our approach offers a more generalized solution to any slu classification task. we automatically derive audio segments at the word level via state-of-the-art speech transcription systems. furthermore, to the best of our knowledge, we are the first to assess the impact of paralinguistic features on predictions in an interpretable form. occluding parts of the input data to measure their impact is a well-established method in xai @xcite . different domains use diverse techniques for removing or masking parts of the data like noise addition, blurring, or masking in grey (vision), a special mask token or removing words (text), or using average values (structured data; @xcite . for speech data, @xcite generate perturbations for a lime explainer using signal zeroing for masking phonemes. notable efforts have analyzed speech models at the subgroup level @xcite @xcite @xcite . these works primarily concentrate on addressing fairness issues and mitigating biases. in contrast, our focus lies on enhancing the interpretability of such systems at both the individual and global levels. ## conclusion we proposed a new perturbation-based explanation method for speech classification models using word-level audio segments and paralinguistic features. the experimental evaluation highlighted the ability of our approach to cope with different tasks, models, and languages. our analysis revealed that word-level attributions accurately identified the spoken words influencing both local and global predictions, aligning well with user expectations. meanwhile, paralinguistic attributions shed light on how non-linguistic features such as prosody and speaking conditions impacted predictions. our findings showed that the generated word-level explanations are faithful to the model's inner workings. moreover, a comprehensive user study proved that the generated explanations are plausible to human experts. users found our explanation representation intuitive for pinpointing relevant words for predictions and examining multiple explanations-a crucial aspect for ensuring their utility in model explanations. future work could further explore explanation assessments, such as the subjective usefulness of explanations and their helpfulness for users for specific practical applications.