Towards Energy-Efficient and Low-Latency Voice-Controlled Smart Homes: A Proposal for Offline Speech Recognition and IoT Integration

The smart home systems, based on AI speech recognition and IoT technology, enable people to control devices through verbal commands and make people's lives more efficient. However, existing AI speech recognition services are primarily deployed on cloud platforms on the Internet. When users issue a command, speech recognition devices like ``Amazon Echo'' will post a recording through numerous network nodes, reach multiple servers, and then receive responses through the Internet. This mechanism presents several issues, including unnecessary energy consumption, communication latency, and the risk of a single-point failure. In this position paper, we propose a smart home concept based on offline speech recognition and IoT technology: 1) integrating offline keyword spotting (KWS) technologies into household appliances with limited resource hardware to enable them to understand user voice commands; 2) designing a local IoT network with decentralized architecture to manage and connect various devices, enhancing the robustness and scalability of the system. This proposal of a smart home based on offline speech recognition and IoT technology will allow users to use low-latency voice control anywhere in the home without depending on the Internet and provide better scalability and energy sustainability.

Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms

Small-Footprint Keyword Spotting (SF-KWS) has gained popularity in today's landscape of smart voice-activated devices, smartphones, and Internet of Things (IoT) applications. This surge is attributed to the advancements in Deep Learning, enabling the identification of predefined words or keywords from a continuous stream of words. To implement the SF-KWS model on edge devices with low power and limited memory in real-world scenarios, a efficient Tiny Machine Learning (TinyML) framework is essential. In this study, we explore seven distinct categories of techniques namely, Model Architecture, Learning Techniques, Model Compression, Attention Awareness Architecture, Feature Optimization, Neural Network Search, and Hybrid Approaches, which are suitable for developing an SF-KWS system. This comprehensive overview will serve as a valuable resource for those looking to understand, utilize, or contribute to the field of SF-KWS. The analysis conducted in this work enables the identification of numerous potential research directions, encompassing insights from automatic speech recognition research and those specifically pertinent to the realm of spoken SF-KWS.

Unified Semi-Supervised Pipeline for Automatic Speech Recognition

Automatic Speech Recognition has been a longstanding research area, with substantial efforts dedicated to integrating semi-supervised learning due to the scarcity of labeled datasets. However, most prior work has focused on improving learning algorithms using existing datasets, without providing a complete public framework for large-scale semi-supervised training across new datasets or languages. In this work, we introduce a fully open-source semi-supervised training framework encompassing the entire pipeline: from unlabeled data collection to pseudo-labeling and model training. Our approach enables scalable dataset creation for any language using publicly available speech data under Creative Commons licenses. We also propose a novel pseudo-labeling algorithm, TopIPL, and evaluate it in both low-resource (Portuguese, Armenian) and high-resource (Spanish) settings. Notably, TopIPL achieves relative WER improvements of 18-40% for Portuguese, 5-16% for Armenian, and 2-8% for Spanish.

Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia

Automatic Speech Recognition (ASR) has transformed daily tasks from video transcription to workplace hiring. ASR systems' growing use warrants robust and standardized auditing approaches to ensure automated transcriptions of high and equitable quality. This is especially critical for people with speech and language disorders (such as aphasia) who may disproportionately depend on ASR systems to navigate everyday life. In this work, we identify three pitfalls in existing standard ASR auditing procedures, and demonstrate how addressing them impacts audit results via a case study of six popular ASR systems' performance for aphasia speakers. First, audits often adhere to a single method of text standardization during data pre-processing, which (a) masks variability in ASR performance from applying different standardization methods, and (b) may not be consistent with how users - especially those from marginalized speech communities - would want their transcriptions to be standardized. Second, audits often display high-level demographic findings without further considering performance disparities among (a) more nuanced demographic subgroups, and (b) relevant covariates capturing acoustic information from the input audio. Third, audits often rely on a single gold-standard metric -- the Word Error Rate -- which does not fully capture the extent of errors arising from generative AI models, such as transcription hallucinations. We propose a more holistic auditing framework that accounts for these three pitfalls, and exemplify its results in our case study, finding consistently worse ASR performance for aphasia speakers relative to a control group. We call on practitioners to implement these robust ASR auditing practices that remain flexible to the rapidly changing ASR landscape.

Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition

This paper presents a novel framework for multi-talker automatic speech recognition without the need for auxiliary information. Serialized Output Training (SOT), a widely used approach, suffers from recognition errors due to speaker assignment failures. Although incorporating auxiliary information, such as token-level timestamps, can improve recognition accuracy, extracting such information from natural conversational speech remains challenging. To address this limitation, we propose Speaker-Distinguishable CTC (SD-CTC), an extension of CTC that jointly assigns a token and its corresponding speaker label to each frame. We further integrate SD-CTC into the SOT framework, enabling the SOT model to learn speaker distinction using only overlapping speech and transcriptions. Experimental comparisons show that multi-task learning with SD-CTC and SOT reduces the error rate of the SOT model by 26% and achieves performance comparable to state-of-the-art methods relying on auxiliary information.

A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments

Speech enhancement, particularly denoising, is vital in improving the intelligibility and quality of speech signals for real-world applications, especially in noisy environments. While prior research has introduced various deep learning models for this purpose, many struggle to balance noise suppression, perceptual quality, and speaker-specific feature preservation, leaving a critical research gap in their comparative performance evaluation. This study benchmarks three state-of-the-art models Wave-U-Net, CMGAN, and U-Net, on diverse datasets such as SpEAR, VPQAD, and Clarkson datasets. These models were chosen due to their relevance in the literature and code accessibility. The evaluation reveals that U-Net achieves high noise suppression with SNR improvements of +71.96% on SpEAR, +64.83% on VPQAD, and +364.2% on the Clarkson dataset. CMGAN outperforms in perceptual quality, attaining the highest PESQ scores of 4.04 on SpEAR and 1.46 on VPQAD, making it well-suited for applications prioritizing natural and intelligible speech. Wave-U-Net balances these attributes with improvements in speaker-specific feature retention, evidenced by VeriSpeak score gains of +10.84% on SpEAR and +27.38% on VPQAD. This research indicates how advanced methods can optimize trade-offs between noise suppression, perceptual quality, and speaker recognition. The findings may contribute to advancing voice biometrics, forensic audio analysis, telecommunication, and speaker verification in challenging acoustic conditions.

EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection

The advancement of text-to-speech and audio generation models necessitates robust benchmarks for evaluating the emotional understanding capabilities of AI systems. Current speech emotion recognition (SER) datasets often exhibit limitations in emotional granularity, privacy concerns, or reliance on acted portrayals. This paper introduces EmoNet-Voice, a new resource for speech emotion detection, which includes EmoNet-Voice Big, a large-scale pre-training dataset (featuring over 4,500 hours of speech across 11 voices, 40 emotions, and 4 languages), and EmoNet-Voice Bench, a novel benchmark dataset with human expert annotations. EmoNet-Voice is designed to evaluate SER models on a fine-grained spectrum of 40 emotion categories with different levels of intensities. Leveraging state-of-the-art voice generation, we curated synthetic audio snippets simulating actors portraying scenes designed to evoke specific emotions. Crucially, we conducted rigorous validation by psychology experts who assigned perceived intensity labels. This synthetic, privacy-preserving approach allows for the inclusion of sensitive emotional states often absent in existing datasets. Lastly, we introduce Empathic Insight Voice models that set a new standard in speech emotion recognition with high agreement with human experts. Our evaluations across the current model landscape exhibit valuable findings, such as high-arousal emotions like anger being much easier to detect than low-arousal states like concentration.

Speech Recognition on TV Series with Video-guided Post-Correction

Automatic Speech Recognition (ASR) has achieved remarkable success with deep learning, driving advancements in conversational artificial intelligence, media transcription, and assistive technologies. However, ASR systems still struggle in complex environments such as TV series, where overlapping speech, domain-specific terminology, and long-range contextual dependencies pose significant challenges to transcription accuracy. Existing multimodal approaches fail to correct ASR outputs with the rich temporal and contextual information available in video. To address this limitation, we propose a novel multimodal post-correction framework that refines ASR transcriptions by leveraging contextual cues extracted from video. Our framework consists of two stages: ASR Generation and Video-based Post-Correction, where the first stage produces the initial transcript and the second stage corrects errors using Video-based Contextual Information Extraction and Context-aware ASR Correction. We employ the Video-Large Multimodal Model (VLMM) to extract key contextual information using tailored prompts, which is then integrated with a Large Language Model (LLM) to refine the ASR output. We evaluate our method on a multimodal benchmark for TV series ASR and demonstrate its effectiveness in improving ASR performance by leveraging video-based context to enhance transcription accuracy in complex multimedia environments.

Technical Report: A Practical Guide to Kaldi ASR Optimization

This technical report introduces innovative optimizations for Kaldi-based Automatic Speech Recognition (ASR) systems, focusing on acoustic model enhancement, hyperparameter tuning, and language model efficiency. We developed a custom Conformer block integrated with a multistream TDNN-F structure, enabling superior feature extraction and temporal modeling. Our approach includes advanced data augmentation techniques and dynamic hyperparameter optimization to boost performance and reduce overfitting. Additionally, we propose robust strategies for language model management, employing Bayesian optimization and $n$-gram pruning to ensure relevance and computational efficiency. These systematic improvements significantly elevate ASR accuracy and robustness, outperforming existing methods and offering a scalable solution for diverse speech recognition scenarios. This report underscores the importance of strategic optimizations in maintaining Kaldi's adaptability and competitiveness in rapidly evolving technological landscapes.

Automatic Speech Recognition of African American English: Lexical and Contextual Effects

Automatic Speech Recognition (ASR) models often struggle with the phonetic, phonological, and morphosyntactic features found in African American English (AAE). This study focuses on two key AAE variables: Consonant Cluster Reduction (CCR) and ING-reduction. It examines whether the presence of CCR and ING-reduction increases ASR misrecognition. Subsequently, it investigates whether end-to-end ASR systems without an external Language Model (LM) are more influenced by lexical neighborhood effect and less by contextual predictability compared to systems with an LM. The Corpus of Regional African American Language (CORAAL) was transcribed using wav2vec 2.0 with and without an LM. CCR and ING-reduction were detected using the Montreal Forced Aligner (MFA) with pronunciation expansion. The analysis reveals a small but significant effect of CCR and ING on Word Error Rate (WER) and indicates a stronger presence of lexical neighborhood effect in ASR systems without LMs.