Speech Intelligibility from Image Processing

Hearing loss research has traditionally been based on perceptual criteria, speech intelligibility and threshold levels. The development of computational models of the auditory-periphery has allowed experimentation via simulation to provide quantitative, repeatable results at a more granular level than would be practical with clinical research on human subjects

Evaluating Sensorineural Hearing Loss With An Auditory Nerve Model Using A Mean Structural Similarity Measure

Hearing loss research has traditionally been based on perceptual criteria, speech intelligibility and threshold levels. The development of computational models of the auditory-periphery has allowed experimentation via simulation to provide quantitative, repeatable results at a more granular level than would be practical with clinical research on human subjects. This work seeks to create an objective measure to automate this inspection process and ranks hearing losses based on auditory-nerve discharge patterns. A systematic way of assessing phonemic degradation using the outputs of an auditory nerve model for a range of sensorineural hearing losses would aid in rapid prototyping development of speech-processing algorithms for digital hearing aids. The effect of sensorineural hearing loss (SNHL) on phonemic structure was evaluated in this study using two types of neurograms: temporal fine structure (TFS) and average discharge rate or temporal envelope. The mean structural similarity index (MSSIM) is an objective measure originally developed to assess perceptual image quality. The measure is adapted here for use in measuring the phonemic degradation in neurograms derived from impaired auditory nerve outputs. A full evaluation of the choice of parameters for the metric is presented using a large amount of natural human speech. The metric’s boundedness and the results for TFS neurograms indicate it is a superior metric to standard point to point metrics of relative mean absolute error and relative mean squared error

Measuring and Monitoring Speech Quality for Voice over IP with POLQA, ViSQOL and P.563

There are many types of degradation which can occur in Voice over IP (VoIP) calls. Of interest in this work are degradations which occur independently of the codec, hardware or network in use. Specifically, their effect on the subjective and objec- tive quality of the speech is examined. Since no dataset suit- able for this purpose exists, a new dataset (TCD-VoIP) has been created and has been made publicly available. The dataset con- tains speech clips suffering from a range of common call qual- ity degradations, as well as a set of subjective opinion scores on the clips from 24 listeners. The performances of three ob- jective quality metrics: POLQA, ViSQOL and P.563, have been evaluated using the dataset. The results show that full reference metrics are capable of accurately predicting a variety of com- mon VoIP degradations. They also highlight the outstanding need for a wideband, single-ended, no-reference metric to mon- itor accurately speech quality for degradations common in VoIP scenarios

The limits of the Mean Opinion Score for speech synthesis evaluation

The release of WaveNet and Tacotron has forever transformed the speech synthesis landscape. Thanks to these game-changing innovations, the quality of synthetic speech has reached unprecedented levels. However, to measure this leap in quality, an overwhelming majority of studies still rely on the Absolute Category Rating (ACR) protocol and compare systems using its output; the Mean Opinion Score (MOS). This protocol is not without controversy, and as the current state-of-the-art synthesis systems now produce outputs remarkably close to human speech, it is now vital to determine how reliable this score is.To do so, we conducted a series of four experiments replicating and following the 2013 edition of the Blizzard Challenge. With these experiments, we asked four questions about the MOS: How stable is the MOS of a system across time? How do the scores of lower quality systems influence the MOS of higher quality systems? How does the introduction of modern technologies influence the scores of past systems? How does the MOS of modern technologies evolve in isolation?The results of our experiments are manyfold. Firstly, we verify the superiority of modern technologies in comparison to historical synthesis. Then, we show that despite its origin as an absolute category rating, MOS is a relative score. While minimal variations are observed during the replication of the 2013-EH2 task, these variations can still lead to different conclusions for the intermediate systems. Our experiments also illustrate the sensitivity of MOS to the presence/absence of lower and higher anchors. Overall, our experiments suggest that we may have reached the end of a cul-de-sac by only evaluating the overall quality with MOS. We must embark on a new road and develop different evaluation protocols better suited to the analysis of modern speech synthesis technologies