Speech recognition error patterns for steady-state noise and interrupted speech

Smith, Kimberly G.; Fogerty, Daniel

doi:10.1121/1.5003916

Cited by 10 publications

(13 citation statements)

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“…Prior work has investigated speech recognition errors in phoneme [36,37,40–42] and word [35,38,39] perception tasks. To the best of our knowledge, only one recent study has examined recognition errors for sentence-level materials [34]. Smith and Fogerty (2017) examined two error categories specifically for the sentence keywords across different non-speech noise contexts (speech in SSN and speech periodically interrupted by SSN with 33%, 50% or 66% speech proportion preserved): Whole word error, which includes substitution, addition, and omission of keywords, and part-word error, which includes substitution, addition, and omission of phonemes in the keywords.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, this study examined speech recognition errors from the SPIN data to understand the mechanisms underlying the hypothesized depression-related listening condition-specific (i.e., speech maskers) deficit in speech perception. In the literature related to SPIN, there is always interest in the examination of the errors in speech recognition produced by listeners, though a limited number of studies have actually implemented speech recognition error analyses [34–43]. The analysis of speech recognition errors can provide information not only about whether a listener recognizes words, but also about how the degraded speech signals are perceived and resolved by the listener [34].…”

Section: Introductionmentioning

confidence: 99%

“…In the literature related to SPIN, there is always interest in the examination of the errors in speech recognition produced by listeners, though a limited number of studies have actually implemented speech recognition error analyses [34–43]. The analysis of speech recognition errors can provide information not only about whether a listener recognizes words, but also about how the degraded speech signals are perceived and resolved by the listener [34]. Thus, the speech recognition error analysis is potentially useful in revealing the mechanisms underlying a listener’s speech perception performance [43].…”

Section: Introductionmentioning

confidence: 99%

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Impact of depression on speech perception in noise

et al. 2019

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Effective speech communication is critical to everyday quality of life and social well-being. In addition to the well-studied deficits in cognitive and motor function, depression also impacts communication. Here, we examined speech perception in individuals who were clinically diagnosed with major depressive disorder (MDD) relative to neurotypical controls. Forty-two normal-hearing (NH) individuals with MDD and 41 NH neurotypical controls performed sentence recognition tasks across three conditions with maskers varying in the extent of linguistic content (high, low, and none): 1-talker masker (1T), reversed 1-talker masker (1T_tr), and speech-shaped noise (SSN). Individuals with MDD, relative to neurotypical controls, demonstrated lower recognition accuracy in the 1T condition but not in the 1T_tr or SSN condition. To examine the nature of the listening condition-specific speech perception deficit, we analyzed speech recognition errors. Errors as a result of interference from masker sentences were higher for individuals with MDD (vs. neurotypical controls) in the 1T condition. This depression-related listening condition-specific pattern in recognition errors was not observed for other error types. We posit that this depression-related listening condition-specific deficit in speech perception may be related to heightened distractibility due to linguistic interference from background talkers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of depression on speech perception in noise

et al. 2019

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“…In the current study, number of phoneme errors was observed to be greater at the lower SNR and when mono-syllabic words were used. Higher number of errors at more severe noise condition (low SNR and minimal availability of perception cues) was reported to be due to greater uncertainty of the target word [24]. Smith and Fogerty [24] stated that the phoneme errors were a result of phonetic confusions that arise in the presence of noise.…”

Section: Discussionmentioning

confidence: 99%

Perception of Tamil Mono-Syllabic and Bi-Syllabic Words in Multi-Talker Speech Babble by Young Adults with Normal Hearing

Gnanasekar¹,

Vaidyanath

2019

J Audiol Otol

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Background and ObjectivesThis study compared the perception of mono-syllabic and bisyllabic words in Tamil by young normal hearing adults in the presence of multi-talker speech babble at two signal-to-noise ratios (SNRs). Further for this comparison, a speech perception in noise test was constructed using existing mono-syllabic and bi-syllabic word lists in Tamil.Subjects and MethodsA total of 30 participants with normal hearing in the age range of 18 to 25 years participated in the study. Speech-in-noise test in Tamil (SPIN-T) constructed using mono-syllabic and bi-syllabic words in Tamil was used as stimuli. The stimuli were presented in the background of multi-talker speech babble at two SNRs (0 dB and +10 dB SNR). ResultsThe effect of noise on SPIN-T varied with SNR. All the participants performed better at +10 dB SNR, the higher of the two SNRs considered. Additionally, at +10 dB SNR performance did not vary significantly for neither mono-syllabic or bi-syllabic words. However, a significant difference existed at 0 dB SNR.ConclusionsThe current study indicated that higher SNR leads to better performance. In addition, bi-syllabic words were identified with minimal errors compared to mono-syllabic words. Spectral cues were the most affected in the presence of noise leading to more of place of articulation errors for both mono-syllabic and bi-syllabic words.

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“…Speech-in-noise perception, particularly in multi-talker environments, is likely dependent on how the AM of competing sources interfere with the recognition of glimpsed speech. Furthermore, while periodic interruption methods are currently used to study a wide variety of effects on speech recognition (e.g., Nagaraj and Knapp, 2015;Wilson and Irish, 2015;Bhargava et al, 2016;Molis and Gallun, 2016;Shafiro et al, 2016;Smiljanic et al, 2016;Smith and Fogerty, 2017); this methodology needs to be verified with more naturalistic conditions where non-periodic speech interruptions are defined according to the temporal intervals of speech glimpses in realistic masking conditions, such as during speech-on-speech masking. This is a more ecologically valid case and necessary to help bridge results from the interrupted speech literature to more naturalistic speech glimpsing models.…”

Section: Introductionmentioning

confidence: 99%

Glimpsing speech interrupted by speech-modulated noise

Miller

Gibbs

Fogerty

2018

The Journal of the Acoustical Society of America

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Everyday environments frequently present speech in modulated noise backgrounds, such as from a competing talker. Under such conditions, temporal glimpses of speech may be preserved at favorable signal-to-noise ratios during the amplitude dips of the masker. Speech recognition is determined, in part, by these speech glimpses. However, properties of the noise when it dominates the speech may also be important. This study interrupted speech to provide either high-intensity or low-intensity speech glimpses derived from measurements of speech-on-speech masking. These interrupted intervals were deleted and subsequently filled by steady-state noise or one of four different types of noise amplitude modulated by the same or different sentence. Noise was presented at two different levels. Interruption by silence was also examined. Speech recognition was best with high-intensity glimpses and improved when the noise was modulated by missing high-intensity segments. Additional noise conditions detailed significant interactions between the noise level and glimpsed speech level. Overall, high-intensity speech segments, and the amplitude modulation (AM) of the segments, are crucial for speech recognition. Speech recognition is further influenced by the properties of the competing noise (i.e., level and AM) which interact with the glimpsed speech level. Acoustic properties of both speech-dominated and noise-dominated intervals of speech-noise mixtures determine speech recognition.

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Speech recognition error patterns for steady-state noise and interrupted speech

Cited by 10 publications

References 19 publications

Impact of depression on speech perception in noise

Impact of depression on speech perception in noise

Perception of Tamil Mono-Syllabic and Bi-Syllabic Words in Multi-Talker Speech Babble by Young Adults with Normal Hearing

Glimpsing speech interrupted by speech-modulated noise

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