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Relating estimated cyclic spectral peak frequency to measured epilarynx length using magnetic resonance imaging

Published in:
INTERSPEECH 2016: 16th Annual Conf. of the Int. Speech Communication Assoc., 8-12 September 2016.

Summary

The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. However, precise acoustic behavior of this lower vocal tract structure is difficult to establish. Focusing on acoustics observable in natural speech, recent spectral processing techniques isolate a unique resonance with characteristics of the epilarynx previously shown via simulation, specifically cyclicity (i.e. energy differences between the closed and open phases of the glottal cycle) in a 3-5kHz region observed across vowels. Using Magnetic Resonance Imaging (MRI), the present work relates this estimated cyclic peak frequency to measured epilarynx length. Assuming a simple quarter wavelength relationship, the cavity length estimated from the cyclic peak frequency is shown to be directly proportional (linear fit slope =1.1) and highly correlated (p = 0.85, pval<10^?4) to the measured epilarynx length across speakers. Results are discussed, as are implications in speech science and application domains.
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Summary

The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. However, precise acoustic behavior of this lower vocal tract structure is difficult to establish. Focusing on acoustics observable in natural speech, recent spectral processing techniques isolate a unique resonance with characteristics...

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Speaker recognition using real vs synthetic parallel data for DNN channel compensation

Published in:
INTERSPEECH 2016: 16th Annual Conf. of the Int. Speech Communication Assoc., 8-12 September 2016.

Summary

Recent work has shown large performance gains using denoising DNNs for speech processing tasks under challenging acoustic conditions. However, training these DNNs requires large amounts of parallel multichannel speech data which can be impractical or expensive to collect. The effective use of synthetic parallel data as an alternative has been demonstrated for several speech technologies including automatic speech recognition and speaker recognition (SR). This paper demonstrates that denoising DNNs trained with real Mixer 2 multichannel data perform only slightly better than DNNs trained with synthetic multichannel data for microphone SR on Mixer 6. Large reductions in pooled error rates of 50% EER and 30% min DCF are achieved using DNNs trained on real Mixer 2 data. Nearly the same performance gains are achieved using synthetic data generated with a limited number of room impulse responses (RIRs) and noise sources derived from Mixer 2. Using RIRs from three publicly available sources used in the Kaldi ASpIRE recipe yields somewhat lower pooled gains of 34% EER and 25% min DCF. These results confirm the effective use of synthetic parallel data for DNN channel compensation even when the RIRs used for synthesizing the data are not particularly well matched to the task.
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Summary

Recent work has shown large performance gains using denoising DNNs for speech processing tasks under challenging acoustic conditions. However, training these DNNs requires large amounts of parallel multichannel speech data which can be impractical or expensive to collect. The effective use of synthetic parallel data as an alternative has been...

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Analysis of factors affecting system performance in the ASpIRE challenge

Published in:
2015 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2015, 13-17 December 2015.

Summary

This paper presents an analysis of factors affecting system performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In particular, overall word error rate (WER) of the solver systems is analyzed as a function of room, distance between talker and microphone, and microphone type. We also analyze speech activity detection performance of the solver systems and investigate its relationship to WER. The primary goal of the paper is to provide insight into the factors affecting system performance in the ASpIRE evaluation set across many systems given annotations and metadata that are not available to the solvers. This analysis will inform the design of future challenges and provide insight into the efficacy of current solutions addressing noisy reverberant speech in mismatched conditions.
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Summary

This paper presents an analysis of factors affecting system performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In particular, overall word error rate (WER) of the solver systems is analyzed as a function of room, distance between talker and microphone, and microphone type. We also analyze speech...

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NetProf iOS pronunciation feedback demonstration

Published in:
IEEE Automatic Speech Recognition and Understanding Workshop, ASRU, 13 December 2015.

Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are not widely studied in the U.S. Many students struggle to attain speaking fluency and proper pronunciation. Teaching pronunciation is a time-intensive task for teachers that requires them to give individual feedback to students during classroom hours. This limits the time teachers can spend imparting other information, and students may feel embarrassed or inhibited when they practice with their classmates. Given the demand for students educated in foreign languages and the limited number of qualified teachers in languages of interest, there is a growing need for computer-based tools students can use to practice and receive feedback at their own pace and schedule. Most existing tools are limited to listening to pre-recorded audio with limited or nonexistent support for pronunciation feedback. MIT Lincoln Laboratory has developed a new tool, Net Pronunciation Feedback (NetProF), to address these challenges and improve student pronunciation and general language fluency.
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Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are...

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