Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comparing models of the combined-stimulation advantage for speech recognition.

ABSTRACT: The "combined-stimulation advantage" refers to an improvement in speech recognition when cochlear-implant or vocoded stimulation is supplemented by low-frequency acoustic information. Previous studies have been interpreted as evidence for "super-additive" or "synergistic" effects in the combination of low-frequency and electric or vocoded speech information by human listeners. However, this conclusion was based on predictions of performance obtained using a suboptimal high-threshold model of information combination. The present study shows that a different model, based on Gaussian signal detection theory, can predict surprisingly large combined-stimulation advantages, even when performance with either information source alone is close to chance, without involving any synergistic interaction. A reanalysis of published data using this model reveals that previous results, which have been interpreted as evidence for super-additive effects in perception of combined speech stimuli, are actually consistent with a more parsimonious explanation, according to which the combined-stimulation advantage reflects an optimal combination of two independent sources of information. The present results do not rule out the possible existence of synergistic effects in combined stimulation; however, they emphasize the possibility that the combined-stimulation advantages observed in some studies can be explained simply by non-interactive combination of two information sources.

SUBMITTER: Micheyl C

PROVIDER: S-EPMC3356316 | biostudies-other | 2012 May

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Infant rule learning: advantage language, or advantage speech?

Project description:Infants appear to learn abstract rule-like regularities (e.g., la la da follows an AAB pattern) more easily from speech than from a variety of other auditory and visual stimuli (Marcus et al., 2007). We test if that facilitation reflects a specialization to learn from speech alone, or from modality-independent communicative stimuli more generally, by measuring 7.5-month-old infants' ability to learn abstract rules from sign language-like gestures. Whereas infants appear to easily learn many different rules from speech, we found that with sign-like stimuli, and under circumstances comparable to those of Marcus et al. (1999), hearing infants were able to learn an ABB rule, but not an AAB rule. This is consistent with results of studies that demonstrate lower levels of infant rule learning from a variety of other non-speech stimuli, and we discuss implications for accounts of speech-facilitation.

| S-EPMC3399874 | biostudies-literature

Comparing Speech Recognition for Listeners With Normal and Impaired Hearing: Simulations for Controlling Differences in Speech Levels and Spectral Shape.

Project description:Purpose This study investigated methods used to simulate factors associated with reduced audibility, increased speech levels, and spectral shaping for aided older adults with hearing loss. Simulations provided to younger normal-hearing adults were used to investigate the effect of sensation level, speech presentation level, and spectral shape in comparison to older adults with hearing loss. Method Measures were assessed in quiet, steady-state noise, and speech-modulated noise. Older adults with hearing loss listened to speech that was spectrally shaped according to their hearing thresholds. Younger adults with normal hearing listened to speech that simulated the hearing-impaired group's (a) reduced audibility, (b) increased speech levels, and (c) spectral shaping. Group comparisons were made based on speech recognition performance and masking release. Additionally, younger adults completed measures of listening effort and perceived speech quality to assess if differences across simulations in these outcome measures were similar to those for speech recognition. Results Across the various simulations employed, testing in the presence of a threshold matching noise best matched differences in speech recognition and masking release between younger and older adults. This result remained consistent across the other two outcome measures. Conclusions A combination of audibility, speech level, and spectral shape factors is required to simulate differences between listeners with normal and impaired hearing in recognition, listening effort, and perceived speech quality. The use of spectrally shaped and amplified speech in the presence of threshold matching noise best provided this simulated control. Supplemental Material https://doi.org/10.23641/asha.13224632.

| S-EPMC8608154 | biostudies-literature

No "Self" Advantage for Audiovisual Speech Aftereffects.

Project description:Although the default state of the world is that we see and hear other people talking, there is evidence that seeing and hearing ourselves rather than someone else may lead to visual (i.e., lip-read) or auditory "self" advantages. We assessed whether there is a "self" advantage for phonetic recalibration (a lip-read driven cross-modal learning effect) and selective adaptation (a contrastive effect in the opposite direction of recalibration). We observed both aftereffects as well as an on-line effect of lip-read information on auditory perception (i.e., immediate capture), but there was no evidence for a "self" advantage in any of the tasks (as additionally supported by Bayesian statistics). These findings strengthen the emerging notion that recalibration reflects a general learning mechanism, and bolster the argument that adaptation depends on rather low-level auditory/acoustic features of the speech signal.

| S-EPMC6440388 | biostudies-literature

Assessment of Dysarthria Using One-Word Speech Recognition with Hidden Markov Models.

Project description:BackgroundThe gold standard in dysarthria assessment involves subjective analysis by a speech-language pathologist (SLP). We aimed to investigate the feasibility of dysarthria assessment using automatic speech recognition.MethodsWe developed an automatic speech recognition based software to assess dysarthria severity using hidden Markov models (HMMs). Word-specific HMMs were trained using the utterances from one hundred healthy individuals. Twenty-eight patients with dysarthria caused by neurological disorders, including stroke, traumatic brain injury, and Parkinson's disease were participated and their utterances were recorded. The utterances of 37 words from the Assessment of Phonology and Articulation for Children test were recorded in a quiet control booth in both groups. Patients were asked to repeat the recordings for evaluating the test-retest reliability. Patients' utterances were evaluated by two experienced SLPs, and the consonant production accuracy was calculated as a measure of dysarthria severity. The trained HMMs were also employed to evaluate the patients' utterances by calculating the averaged log likelihood (aLL) as the fitness of the spoken word to the word-specific HMM.ResultsThe consonant production accuracy reported by the SLPs strongly correlated (r = 0.808) with the aLL, and the aLL showed excellent test-retest reliability (intraclass correlation coefficient, 0.964).ConclusionThis leads to the conclusion that dysarthria assessment using a one-word speech recognition system based on word-specific HMMs is feasible in neurological disorders.

| S-EPMC6449601 | biostudies-literature

Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition.

Project description:The presented paper introduces principal component analysis application for dimensionality reduction of variables describing speech signal and applicability of obtained results for the disturbed and fluent speech recognition process. A set of fluent speech signals and three speech disturbances-blocks before words starting with plosives, syllable repetitions, and sound-initial prolongations-was transformed using principal component analysis. The result was a model containing four principal components describing analysed utterances. Distances between standardised original variables and elements of the observation matrix in a new system of coordinates were calculated and then applied in the recognition process. As a classifying algorithm, the multilayer perceptron network was used. Achieved results were compared with outcomes from previous experiments where speech samples were parameterised with the Kohonen network application. The classifying network achieved overall accuracy at 76% (from 50% to 91%, depending on the dysfluency type).

| S-EPMC8749906 | biostudies-literature

Speech perception in noise: Impact of directional microphones in users of combined electric-acoustic stimulation.

Project description:OBJECTIVES:Combined electric-acoustic stimulation (EAS) is a well-accepted therapeutic treatment for cochlear implant (CI) users with residual hearing in the low frequencies but severe to profound hearing loss in the high frequencies. The recently introduced SONNETeas audio processor offers different microphone directionality (MD) settings and wind noise reduction (WNR) as front-end processing. The aim of this study was to compare speech perception in quiet and noise between two EAS audio processors DUET 2 and SONNETeas, to assess the impact of MD and WNR on speech perception in EAS users in the absence of wind. Furthermore, subjective rating of hearing performance was registered. METHOD:Speech perception and subjective rating with SONNETeas or DUET 2 audio processor were assessed in 10 experienced EAS users. Speech perception was measured in quiet and in a diffuse noise setup (MSNF). The SONNETeas processor was tested with three MD settings omnidirectional/natural/adaptive and with different intensities of WNR. Subjective rating of auditory benefit and sound quality was rated using two questionnaires. RESULTS:There was no significant difference between DUET 2 and SONNETeas processor using the omnidirectional microphone in quiet and in noise. There was a significant improvement in SRT with MD settings natural (2.2 dB) and adaptive (3.6 dB). No detrimental effect of the WNR algorithm on speech perception was found in the absence of wind. Sound quality was rated as "moderate" for both audio processors. CONCLUSIONS:The different MD settings of the SONNETeas can provide EAS users with better speech perception compared to an omnidirectional microphone. Concerning speech perception in quiet and quality of life, the performance of the DUET 2 and SONNETeas audio processors was comparable.

| S-EPMC6402691 | biostudies-literature

Comparing the effects of reverberation and of noise on speech recognition in simulated electric-acoustic listening.

Project description:Cochlear implant users report difficulty understanding speech in both noisy and reverberant environments. Electric-acoustic stimulation (EAS) is known to improve speech intelligibility in noise. However, little is known about the potential benefits of EAS in reverberation, or about how such benefits relate to those observed in noise. The present study used EAS simulations to examine these questions. Sentences were convolved with impulse responses from a model of a room whose estimated reverberation times were varied from 0 to 1 sec. These reverberated stimuli were then vocoded to simulate electric stimulation, or presented as a combination of vocoder plus low-pass filtered speech to simulate EAS. Monaural sentence recognition scores were measured in two conditions: reverberated speech and speech in a reverberated noise. The long-term spectrum and amplitude modulations of the noise were equated to the reverberant energy, allowing a comparison of the effects of the interferer (speech vs noise). Results indicate that, at least in simulation, (1) EAS provides significant benefit in reverberation; (2) the benefits of EAS in reverberation may be underestimated by those in a comparable noise; and (3) the EAS benefit in reverberation likely arises from partially preserved cues in this background accessible via the low-frequency acoustic component.

| S-EPMC3283901 | biostudies-other

Real and synthetic Punjabi speech datasets for automatic speech recognition.

Project description:Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.

| S-EPMC10749247 | biostudies-literature

Influence of Electric Frequency-to-Place Mismatches on the Early Speech Recognition Outcomes for Electric-Acoustic Stimulation Users.

Project description:PurposeCochlear implant (CI) recipients with hearing preservation experience significant improvements in speech recognition with electric-acoustic stimulation (EAS) as compared to with a CI alone, although outcomes across EAS users vary. The individual differences in performance may be due in part to default mapping procedures, which result in electric frequency-to-place mismatches for the majority of EAS users. This study assessed the influence of electric mismatches on the early speech recognition for EAS users.MethodTwenty-one participants were randomized at EAS activation to listen exclusively with a default or place-based map. For both groups, the unaided thresholds determined the acoustic cutoff frequency (i.e., > 65 dB HL). For default maps, the electric filter frequencies were assigned to avoid spectral gaps in frequency information but created varying magnitudes of mismatches. For place-based maps, the electric filter frequencies were assigned to avoid frequency-to-place mismatches. Recognition of consonant-nucleus-consonant words and vowels was assessed at activation and 1, 3, and 6 months postactivation.ResultsFor participants with default maps, electric mismatch at 1500 Hz ranged from 2 to -12.0 semitones (Mdn = -5 semitones). Poorer performance was observed for those with larger magnitudes of electric mismatch. This effect was observed through 6 months of EAS listening experience.ConclusionsThe present sample of EAS users experienced better initial performance when electric mismatches were small or eliminated. These data suggest the utility of methods that reduce electric mismatches, such as place-based mapping procedures. Investigation is ongoing to determine whether these differences persist with long-term EAS use.Supplemental materialhttps://doi.org/10.23641/asha.22096523.

| S-EPMC10166189 | biostudies-literature

Racial disparities in automated speech recognition.

Project description:Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems. There is concern, however, that these tools do not work equally well for all subgroups of the population. Here, we examine the ability of five state-of-the-art ASR systems-developed by Amazon, Apple, Google, IBM, and Microsoft-to transcribe structured interviews conducted with 42 white speakers and 73 black speakers. In total, this corpus spans five US cities and consists of 19.8 h of audio matched on the age and gender of the speaker. We found that all five ASR systems exhibited substantial racial disparities, with an average word error rate (WER) of 0.35 for black speakers compared with 0.19 for white speakers. We trace these disparities to the underlying acoustic models used by the ASR systems as the race gap was equally large on a subset of identical phrases spoken by black and white individuals in our corpus. We conclude by proposing strategies-such as using more diverse training datasets that include African American Vernacular English-to reduce these performance differences and ensure speech recognition technology is inclusive.

| S-EPMC7149386 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data