Multi-objective Non-intrusive Hearing-aid Speech Assessment Model

Without the need for a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. While deep learning models have been used to develop non-intrusive speech assessment methods with promising results, there is limited research on hearing-impaired subjects. This study proposes a multi-objective non-intrusive hearing-aid speech assessment model, called HASA-Net Large, which predicts speech quality and intelligibility scores based on input speech signals and specified hearing-loss patterns. Our experiments showed the utilization of pre-trained SSL models leads to a significant boost in speech quality and intelligibility predictions compared to using spectrograms as input. Additionally, we examined three distinct fine-tuning approaches that resulted in further performance improvements. Furthermore, we demonstrated that incorporating SSL models resulted in greater transferability to OOD dataset. Finally, this study introduces HASA-Net Large, which is a non-invasive approach for evaluating speech quality and intelligibility. HASA-Net Large utilizes raw waveforms and hearing-loss patterns to accurately predict speech quality and intelligibility levels for individuals with normal and impaired hearing and demonstrates superior prediction performance and transferability

InQSS: a speech intelligibility and quality assessment model using a multi-task learning network

Speech intelligibility and quality assessment models are essential tools for researchers to evaluate and improve speech processing models. However, only a few studies have investigated multi-task models for intelligibility and quality assessment due to the limitations of available data. In this study, we released TMHINT-QI, the first Chinese speech dataset that records the quality and intelligibility scores of clean, noisy, and enhanced utterances. Then, we propose InQSS, a non-intrusive multi-task learning framework for intelligibility and quality assessment. We evaluated the InQSS on both the training-from-scratch and the pretrained models. The experimental results confirm the effectiveness of the InQSS framework. In addition, the resulting model can predict not only the intelligibility scores but also the quality scores of a speech signal.Comment: accepted by Insterspeech 202

An evaluation of intrusive instrumental intelligibility metrics

Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and $\text{sEPSM}^\text{corr}$. In addition, this paper investigates the ability of intelligibility metrics to generalize to new types of distortions and analyzes why the top performing metrics have high performance. The intelligibility data were obtained from 11 listening tests described in the literature. The stimuli included Dutch, Danish, and English speech that was distorted by additive noise, reverberation, competing talkers, pre-processing enhancement, and post-processing enhancement. SIIB and HASPI had the highest performance achieving a correlation with listening test scores on average of $\rho=0.92$ and $\rho=0.89$, respectively. The high performance of SIIB may, in part, be the result of SIIBs developers having access to all the intelligibility data considered in the evaluation. The results show that intelligibility metrics tend to perform poorly on data sets that were not used during their development. By modifying the original implementations of SIIB and STOI, the advantage of reducing statistical dependencies between input features is demonstrated. Additionally, the paper presents a new version of SIIB called $\text{SIIB}^\text{Gauss}$, which has similar performance to SIIB and HASPI, but takes less time to compute by two orders of magnitude.Comment: Published in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 201

Speech assessment and characterization for law enforcement applications

Speech signals acquired, transmitted or stored in non-ideal conditions are often degraded by one or more effects including, for example, additive noise. These degradations alter the signal properties in a manner that deteriorates the intelligibility or quality of the speech signal. In the law enforcement context such degradations are commonplace due to the limitations in the audio collection methodology, which is often required to be covert. In severe degradation conditions, the acquired signal may become unintelligible, losing its value in an investigation and in less severe conditions, a loss in signal quality may be encountered, which can lead to higher transcription time and cost. This thesis proposes a non-intrusive speech assessment framework from which algorithms for speech quality and intelligibility assessment are derived, to guide the collection and transcription of law enforcement audio. These methods are trained on a large database labelled using intrusive techniques (whose performance is verified with subjective scores) and shown to perform favorably when compared with existing non-intrusive techniques. Additionally, a non-intrusive CODEC identification and verification algorithm is developed which can identify a CODEC with an accuracy of 96.8 % and detect the presence of a CODEC with an accuracy higher than 97 % in the presence of additive noise. Finally, the speech description taxonomy framework is developed, with the aim of characterizing various aspects of a degraded speech signal, including the mechanism that results in a signal with particular characteristics, the vocabulary that can be used to describe those degradations and the measurable signal properties that can characterize the degradations. The taxonomy is implemented as a relational database that facilitates the modeling of the relationships between various attributes of a signal and promises to be a useful tool for training and guiding audio analysts

Improved status following behavioural intervention in a case of severe dysarthria with stroke aetiology

There is little published intervention outcome literature concerning dysarthria acquired from stroke. Single case studies have the potential to provide more detailed specification and interpretation than is generally possible with larger participant numbers and are thus informative for clinicians who may deal with similar cases. Such research also contributes to the future planning of larger scale investigations. Behavioural intervention is described which was carried out with a man with severe dysarthria following stroke, beginning at seven and ending at nine months after stroke. Pre-intervention stability between five and seven months contrasted with significant improvements post-intervention on listener-rated measures of word and reading intelligibility and communication effectiveness in conversation. A range of speech analyses were undertaken (comprising of rate, pause and intonation characteristics in connected speech and phonetic transcription of single word production), with the aim of identifying components of speech which might explain the listeners’ perceptions of improvement. Pre- and post intervention changes could be detected mainly in parameters related to utterance segmentation and intonation. The basis of improvement in dysarthria following intervention is complex, both in terms of the active therapeutic dimensions and also the specific speech alterations which account for changes to intelligibility and effectiveness. Single case results are not necessarily generalisable to other cases and outcomes may be affected by participant factors and therapeutic variables, which are not readily controllable

E-model modification for case of cascade codecs arrangement

Speech quality assessment is one of the key matters of voice services and every provider should ensure adequate connection quality to end users. Speech quality has to be measured by a trusted method and results have to correlate with intelligibility and clarity of the speech, as perceived by the listener. It can be achieved by subjective methods but in real life we must rely on objective measurements based on reliable models. One of them is E-model that we can consider as mainly adopted method in IP telephony. This method is based on evaluation of transmission path impairments influencing speech signal, especially delays and packet losses. These parameters which are common in IP network can affect dramatically speech quality. In this article, a new modification of E-model, that takes into consideration the cascade codecs arrangement, is presented. The proposed a correction function improves the current computational non-intrusive approach that is described in recommendation ITU-T G.107, so-called E-model.Scopus551447143