scholarly journals On integrating insights from human speech perception into automatic speech recognition

2005 ◽  
Author(s):  
Sorin Dusan ◽  
Larry R. Rabiner
Keyword(s):  
Speech Recognition ◽  
Speech Perception ◽  
Automatic Speech Recognition ◽  
Human Speech

scholarly journals Structure in talker variability: How much is there and how much can it help?

2018 ◽  
Author(s):  
Dave F Kleinschmidt
Keyword(s):  
Speech Recognition ◽  
Speech Perception ◽  
R Package ◽  
Ideal Observer ◽  
Linguistic Variation ◽  
Robust Speech Recognition ◽  
Talker Variability ◽  
New Techniques ◽  
Human Speech ◽  
The Face

One of the persistent puzzles in understanding human speech perception is how listeners cope with talker variability. One thing that might help listeners is structure in talker variability: rather than varying randomly, talkers of the same gender, dialect, age, etc. tend to produce language in similar ways. Sociolinguistic research has shown that listeners are sensitive to this covariation between linguistic variation and socio-indexical variables. In this paper I present new techniques based on ideal observer models to quantify 1) the amount and type of structure in talker variation, and 2) how useful such structure can be for robust speech recognition in the face of talker variability. I demonstrate these techniques in two phonetic domains---word-initial stop voicing and vowel identity---and show that these domains have different amounts and types of talker variability, consistent with previous, impressionistic findings. An `R` package accompanies this paper, enabling researchers to apply these techniques to their own data.

scholarly journals Relating dynamic brain states to dynamic machine states: human and machine solutions to the speech recognition problem

2016 ◽  
Author(s):  
Cai Wingfield ◽  
Li Su ◽  
Xunying Liu ◽  
Chao Zhang ◽  
Phil Woodland ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Human Brain ◽  
Automatic Speech Recognition ◽  
Temporal Cortex ◽  
Human Cognition ◽  
Speech Comprehension ◽  
Human Speech ◽  
Internal States ◽  
Brain States ◽  
Phonetic Features

AbstractThere is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro-and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.Author SummaryThe ability to understand spoken language is a defining human capacity. But despite decades of research, there is still no well-specified account of how sound entering the ear is neurally interpreted as a sequence of meaningful words. At the same time, modern computer-based Automatic Speech Recognition (ASR) systems are capable of nearhuman levels of performance, especially where word-identification is concerned. In this research we aim to bridge the gap between human and machine solutions to speech recognition. We use a novel combination of neuroimaging and statistical methods to relate human and machine internal states that are dynamically generated as spoken words are heard by human listeners and analysed by ASR systems. We find that the stable regularities discovered by the ASR process, linking speech input to phonetic labels, can be significantly related to the regularities extracted in the human brain. Both systems may have in common a representation of these regularities in terms of articulatory phonetic features, consistent with an analysis process which recovers the articulatory gestures that generated the speech. These results suggest a possible partnership between human-and machine-based research which may deliver both a better understanding of how the human brain provides such a robust solution to speech understanding, and generate insights that enhance the performance of future ASR systems.

Issues Relating to Automatic Speech Recognition and Spatial Auditory Displays in High Noise, Stressful Tank Environments

2000 ◽  
Vol 44 (22) ◽  
pp. 754-757 ◽  
Author(s):  
Ellen Haas ◽  
Robert Shankle ◽  
Hattie Murray ◽  
Deborah Travers ◽  
Ted Wheeler
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Radio Channel ◽  
Spatial Audio ◽  
High Noise ◽  
Channel Changes ◽  
Human Speech ◽  
Independent Variables ◽  
Speech Output ◽  
Information Input

The purpose of this study was to determine the extent to which automatic speech recognition (ASR) systems and human speech output to ASR systems are affected when users perform complex tasks in a noisy, stressful simulated tank environment. A second objective was to determine whether the type of auditory information input (monaural or spatial) affected user task performance, including the output of speech to ASR systems. Subjects performed simultaneous radio communication and tank driving tasks. Independent variables included auditory display configuration, driving speed, and subject type (soldier or civilian). Dependent variables included 1) the proportion of radio channel changes correctly recognized by the ASR; 2) the proportion and types of speech errors in radio channel changes; 3) the number of accurate responses to the radio messages; and 4) subject ratings of stress and workload. Subjects were six male U.S. Army personnel and six male civilians. Results indicated that ASR and human speech output were not significantly affected by any of the independent variables. A mean of 95.5% of all subject verbal commands was recognized by the ASR system. Spatialized radio messages significantly enhanced the ability of the user to understand speech communications from multiple channels. However, spatial audio information input had no significant effect on user speech output or on ASR performance. In general, ASR and spatial audio displays have potential as technologies of interest in high noise, stressful tank environments in which complex, concurrent tasks are performed.

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