Neural network based approaches to speech enhancement have shown to be particularly powerful, being able to leverage a data-driven approach to result in a significant performance gain versus other approaches. Such approaches are reliant on artificially created labelled training data such that the neural model can be trained using intrusive loss functions which compare the output of the model with clean reference speech. Performance of such systems when enhancing real-world audio often suffers relative to their performance on simulated test data. In this work, a non-intrusive multi-metric prediction approach is introduced, wherein a model trained on artificial labelled data using inference of an adversarially trained metric prediction neural network. The proposed approach shows improved performance versus state-of-the-art systems on the recent CHiME-7 challenge unsupervised domain adaptation speech enhancement (UDASE) task evaluation sets. Index Terms: speech enhancement, model generalisation, generative adversarial networks, conformer, metric prediction

Neural networks have been successfully used for non-intrusive speech intelligibility prediction. Recently, the use of feature representations sourced from intermediate layers of pre-trained self-supervised and weakly-supervised models has been found to be particularly useful for this task. This work combines the use of Whisper ASR decoder layer representations as neural network input features with an exemplar-based, psychologically motivated model of human memory to predict human intelligibility ratings for hearing-aid users. Substantial performance improvement over an established intrusive HASPI baseline system is found, including on enhancement systems and listeners unseen in the training data, with a root mean squared error of 25.3 compared with the baseline of 28.7.

One solution to automatic speech recognition (ASR) of overlapping speakers is to separate speech and then perform ASR on the separated signals.Commonly, the separator produces artefacts which often degrade ASR performance.Addressing this issue typically requires reference transcriptions to jointly train the separation and ASR networks.This is often not viable for training on real-world in-domain audio where reference transcript information is not always available.This paper proposes a transcription-free method for joint training using only audio signals.The proposed method uses embedding differences of pre-trained ASR encoders as a loss with a proposed modification to permutation invariant training (PIT) called guided PIT (GPIT).The method achieves a 6.4% improvement in word error rate (WER) measures over a signal-level loss and also shows enhancement improvements in perceptual measures such as short-time objective intelligibility (STOI).

Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models. However, much of this work focuses on using the deepest or final outputs of self supervised speech representation models, rather than the earlier feature encodings. The use of self supervised representations in such a way is often not fully motivated. In this work it is shown that the distance between the feature encodings of clean and noisy speech correlate strongly with psychoacoustically motivated measures of speech quality and intelligibility, as well as with human Mean Opinion Score (MOS) ratings. Experiments using this distance as a loss function are performed and improved performance over the use of STFT spectrogram distance based loss as well as other common loss functions from speech enhancement literature is demonstrated using objective measures such as perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI).

This paper proposes neural models to predict Speech Intelligibility (SI),both by prediction of established SI metrics and of human speech recognition (HSR) on the 1st Clarity Prediction Challenge. Both intrusive and non-intrusive predictors for intrusive SI metrics are trained, then fine tuned on the HSR ground truth. Results are reported on a number of SI metrics, and the model choice for the Clarity challenge submission is explained. Additionally, the relationship between the SI scores in the data and commonly used signal processing metrics which approximate SI are analysed, and some issues emerging from this relationship discussed. It is found that intrusive neural predictors of SI metrics when finetuned on the true HSR scores outperform the non neural challenge baseline.