A perceptual masking approach for noise robust speech recognition

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Verification of feature regions for stops and fricatives in natural speech

The presence of acoustic cues and their importance in speech perception have long remained debatable topics. In spite of several studies that exist in this eld, very little is known about what exactly humans perceive in speech. This research takes a novel approach towards understanding speech perception. A new method, named three-dimensional deep search (3DDS), was developed to explore the perceptual cues of 16 consonant-vowel (CV) syllables, namely /pa/, /ta/, /ka/, /ba/, /da/, /ga/, /fa/, /Ta/, /sa/, /Sa/, /va/, /Da/, /za/, /Za/, from naturally produced speech. A veri cation experiment was then conducted to further verify the ndings of the 3DDS method. For this pur- pose, the time-frequency coordinate that de nes each CV was ltered out using the short-time Fourier transform (STFT), and perceptual tests were then conducted. A comparison between unmodi ed speech sounds and those without the acoustic cues was made. In most of the cases, the scores dropped from 100% to chance levels even at 12 dB SNR. This clearly emphasizes the importance of features in identifying each CV. The results con rm earlier ndings that stops are characterized by a short-duration burst preceding the vowel by 10 cs in the unvoiced case, and appearing almost coincident with the vowel in the voiced case. As has been previously hypothesized, we con rmed that the F2 transition plays no signi cant role in consonant identi cation. 3DDS analysis labels the /sa/ and /za/ perceptual features as an intense frication noise around 4 kHz, preceding the vowel by 15{20 cs, with the /za/ feature being around 5 cs shorter in duration than that of /sa/; the /Sa/ and /Za/ events are found to be frication energy near 2 kHz, preceding the vowel by 17{20 cs. /fa/ has a relatively weak burst and frication energy over a wide-band including 2{6 kHz, while /va/ has a cue in the 1.5 kHz mid-frequency region preceding the vowel by 7{10 cs. New information is established regarding /Da/ and /Ta/, especially with regards to the nature of their signi cant confusions

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks