Koklear İmplant Konuşma İşlemcileri için Optimum Parametrelerin Objektif Ölçütler Kullanılarak Belirlenmesi

In a cochlear implant (CI) speech processor, several parameters such as channel numbers, bandwidths, rectification type, and cutoff frequency play an important role in acquiring enhanced speech. The effective and general purpose CI approach has been a research topic for a long time. In this study, it is aimed to determine the optimum parameters for CI users by using different channel numbers (4, 8, 12, 16, and 22), rectification types (half and full) and cutoff frequencies (200, 250, 300, 350, and 400 Hz). The CI approaches have been tested on Turkish sentences which are taken from METU database. The optimum CI structure has been tested with objective quality that weighted spectral slope (WSS) and objective intelligibility measures such as short-term objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ). Experimental results show that 400 Hz cutoff frequency, full wave rectifier, and 16-channels CI approach give better quality and higher intelligibility scores than other CI approaches according to STOI, PESQ and WSS results. The proposed CI approach provides the ability to percept 91% of output vocoded Turkish speech for CI users. © 2022, TUBITAK. All rights reserved

Speech Enhancement Guided by Contextual Articulatory Information

Previous studies have confirmed the effectiveness of leveraging articulatory information to attain improved speech enhancement (SE) performance. By augmenting the original acoustic features with the place/manner of articulatory features, the SE process can be guided to consider the articulatory properties of the input speech when performing enhancement. Hence, we believe that the contextual information of articulatory attributes should include useful information and can further benefit SE in different languages. In this study, we propose an SE system that improves its performance through optimizing the contextual articulatory information in enhanced speech for both English and Mandarin. We optimize the contextual articulatory information through joint-train the SE model with an end-to-end automatic speech recognition (E2E ASR) model, predicting the sequence of broad phone classes (BPC) instead of the word sequences. Meanwhile, two training strategies are developed to train the SE system based on the BPC-based ASR: multitask-learning and deep-feature training strategies. Experimental results on the TIMIT and TMHINT dataset confirm that the contextual articulatory information facilitates an SE system in achieving better results than the traditional Acoustic Model(AM). Moreover, in contrast to another SE system that is trained with monophonic ASR, the BPC-based ASR (providing contextual articulatory information) can improve the SE performance more effectively under different signal-to-noise ratios(SNR).Comment: Will be submitted to TASL

On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement

Many deep learning-based speech enhancement algorithms are designed to minimize the mean-square error (MSE) in some transform domain between a predicted and a target speech signal. However, optimizing for MSE does not necessarily guarantee high speech quality or intelligibility, which is the ultimate goal of many speech enhancement algorithms. Additionally, only little is known about the impact of the loss function on the emerging class of time-domain deep learning-based speech enhancement systems. We study how popular loss functions influence the performance of deep learning-based speech enhancement systems. First, we demonstrate that perceptually inspired loss functions might be advantageous if the receiver is the human auditory system. Furthermore, we show that the learning rate is a crucial design parameter even for adaptive gradient-based optimizers, which has been generally overlooked in the literature. Also, we found that waveform matching performance metrics must be used with caution as they in certain situations can fail completely. Finally, we show that a loss function based on scale-invariant signal-to-distortion ratio (SI-SDR) achieves good general performance across a range of popular speech enhancement evaluation metrics, which suggests that SI-SDR is a good candidate as a general-purpose loss function for speech enhancement systems.Comment: Published in the IEEE Transactions on Audio, Speech and Language Processin