Speech enhancement models optimised for perceptual quality metrics such as PESQ do not necessarily improve automatic speech recognition (ASR) performance, as aggressive noise suppression can distort the phonemic cues that acoustic models rely on. We propose a time-domain speech frontend based on the Mamba selective state space model, trained exclusively with ASR-oriented loss functions to preserve phonetically discriminative structure rather than maximise perceptual quality. The model follows a U-Net encoderdecoder architecture with 4 × temporal downsampling and six stacked bidirectional Mamba blocks operating on the raw waveform, trained with a multi-component loss combining multiresolution spectral supervision, log-mel feature matching, speech correlation preservation, and ideal-ratio-mask spectral objectives. On VoiceBank-DEMAND, the proposed model achieves 10.26% WER with wav2vec2-base-960h and 7.68% with whisper-base, achieving statistically equivalent WER to SE-Mamba (10.30% / 7.73%) while producing a lower PESQ of 3.10 versus 3.69. Phoneme-class analysis confirms that the proposed model matches or outperforms SEMamba on five of seven articulatory classes, demonstrating that ASR-oriented training objectives achieve equivalent robustness to perceptual enhancement models without modifying the downstream ASR system.
Reshma K R, A. Vuppala· International Conference on...· 0 citations
Current systems for stuttering detection are limited by task-agnostic feature compression and flat layer-fusion strategies. Traditional methods rely on Principal Component Analysis (PCA) to reduce self-supervised representations, which often suppresses subtle disfluency patterns in favor of speakeridentity cues. We propose ResGDS-HLF, a framework that replaces static compression with Residual Gumbel Dimension Selection to recover discriminative dimensions directly optimized for stuttering classification. By partitioning the transformer backbone into a functional hierarchy, i.e. acoustic, phonetic, and lexical, the model preserves specialized information that is otherwise diluted by weighted averaging. Evaluated on the SEP-28K corpus, ResGDS-HLF achieves a Macro-F1 of 0.61, representing an absolute gain of 0.14 over single-layer PCA baselines. Zero-shot transfer to FluencyBank confirms that taskdriven Gumbel selection captures robust, generalizable disfluency signatures across diverse acoustic environments.
Pragya Khanna, Swathi Sambangi, Vijaya Saraswathi R et al.· International Conference on...· 0 citations
Text-to-speech systems are improving fast, but measuring how natural they sound still requires expensive human listening tests. Existing automatic methods struggle to generalise well across different datasets. We present QMOS, a MOS prediction framework that extracts hierarchical speech quality features from a frozen Qwen2-Audio large audio language model and combines them with layer-weighted WavLM SSL representations through a learned cross-attention fusion. This design captures both high-level semantic naturalness and lowlevel acoustic distortions in a unified model. On two standard benchmarks, SOMOS and BVCC, QMOS achieves competitive system-level SRCC of 0.923 and 0.921 on SOMOS and BVCC, respectively, while using no system-ID conditioning or listener embeddings. Cross-domain evaluation yields a competitive SRCC of 0.702, suggesting the learned representations generalise across acoustic domains.
Sri Ravi Sastry Kolluru, Charan Devarakonda, S. Radhe et al.· International Conference on...· 0 citations
Developing Automatic Speech Recognition (ASR) systems for specialized medical domains is challenging in lowresource settings due to the limited availability of annotated speech data. Synthetic speech generated using text to speech (TTS) systems is often used to augment training data, but directly mixing synthetic and real speech can introduce distribution mismatch that degrades encoder representations. This work introduces Real-Governed Representation-Calibrated Training (RG-RCT), a training strategy that regulates the influence of synthetic speech at the representation level. The proposed framework combines reliability-based confidence estimation, representation alignment, and layer-wise representation governance to stabilize the encoder feature space while preserving acoustic diversity from multi-speaker synthetic data. Experiments on medical-domain speech datasets using Wav2Vec2 and Whisper demonstrate that RG-RCT consistently outperforms conventional strategies such as direct mixing and confidence-aware training. The proposed method achieves the lowest Word Error Rate (WER), reaching 17.1% and 16.6% for Telugu and 23.3% and 25.6% for Kannada. These results indicate that regulating encoder representation geometry enables more effective utilization of synthetic speech for low-resource medical ASR.
S. Devarakonda, Ravi Sastry Kolluru, Srihari Bandarupalli et al.· International Conference on...· 0 citations