Real-Time Contrast Enhancement to Improve Speech Recognition
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{"title"=>"Real-time contrast enhancement to improve speech recognition", "type"=>"journal", "authors"=>[{"first_name"=>"Joshua M.", "last_name"=>"Alexander", "scopus_author_id"=>"8638486700"}, {"first_name"=>"Rick L.", "last_name"=>"Jenison", "scopus_author_id"=>"7003330926"}, {"first_name"=>"Keith R.", "last_name"=>"Kluender", "scopus_author_id"=>"6603903353"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-80052913125", "pmid"=>"21949736", "sgr"=>"80052913125", "doi"=>"10.1371/journal.pone.0024630", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "issn"=>"19326203", "pui"=>"362577551"}, "id"=>"2aad2581-855a-3f10-8f66-7417fd6360cb", "abstract"=>"An algorithm that operates in real-time to enhance the salient features of speech is described and its efficacy is evaluated. The Contrast Enhancement (CE) algorithm implements dynamic compressive gain and lateral inhibitory sidebands across channels in a modified winner-take-all circuit, which together produce a form of suppression that sharpens the dynamic spectrum. Normal-hearing listeners identified spectrally smeared consonants (VCVs) and vowels (hVds) in quiet and in noise. Consonant and vowel identification, especially in noise, were improved by the processing. The amount of improvement did not depend on the degree of spectral smearing or talker characteristics. For consonants, when results were analyzed according to phonetic feature, the most consistent improvement was for place of articulation. This is encouraging for hearing aid applications because confusions between consonants differing in place are a persistent problem for listeners with sensorineural hearing loss.", "link"=>"http://www.mendeley.com/research/realtime-contrast-enhancement-improve-speech-recognition", "reader_count"=>34, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>12, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>5, "Student > Bachelor"=>4, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>12, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>5, "Student > Bachelor"=>4, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>10, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>6, "Agricultural and Biological Sciences"=>2, "Neuroscience"=>2, "Psychology"=>4, "Chemistry"=>1, "Computer Science"=>6, "Linguistics"=>1, "Physics and Astronomy"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>10}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Neuroscience"=>{"Neuroscience"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Psychology"=>{"Psychology"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>6}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Linguistics"=>{"Linguistics"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/370394", "https://ndownloader.figshare.com/files/370540"], "description"=>"<div><p>An algorithm that operates in real-time to enhance the salient features of speech is described and its efficacy is evaluated. The Contrast Enhancement (CE) algorithm implements dynamic compressive gain and lateral inhibitory sidebands across channels in a modified winner-take-all circuit, which together produce a form of suppression that sharpens the dynamic spectrum. Normal-hearing listeners identified spectrally smeared consonants (VCVs) and vowels (hVds) in quiet and in noise. Consonant and vowel identification, especially in noise, were improved by the processing. The amount of improvement did not depend on the degree of spectral smearing or talker characteristics. For consonants, when results were analyzed according to phonetic feature, the most consistent improvement was for place of articulation. This is encouraging for hearing aid applications because confusions between consonants differing in place are a persistent problem for listeners with sensorineural hearing loss.</p> </div>", "links"=>[], "tags"=>["Real-time", "enhancement"], "article_id"=>133186, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024630.s001", "https://dx.doi.org/10.1371/journal.pone.0024630.s002"], "stats"=>{"downloads"=>8, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Real_Time_Contrast_Enhancement_to_Improve_Speech_Recognition/133186", "title"=>"Real-Time Contrast Enhancement to Improve Speech Recognition", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-09-19 00:53:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/734080"], "description"=>"<p>(a) Unenhanced spatiotemporal signal. (b) WTA enhanced spatiotemporal signal demonstrating spectral sharpening and expansion of the position of the spectral peaks as a consequence of dynamic competition between subband channels. (c) Cross-section of channels at 150 ms for unenhanced signals (blue), dynamically enhanced (red), and instantaneously enhanced (black) spatiotemporal signals illustrating spatial (spectral) sharpening. (d) Cross-section of channels at 370 ms for unenhanced (blue), dynamically enhanced (red), and instantaneously enhanced (black) spatiotemporal signals illustrating sharpening and expansion of spectral peaks.</p>", "links"=>[], "tags"=>["wta"], "article_id"=>404448, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulated_input_to_the_WTA_network_/404448", "title"=>"Simulated input to the WTA network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:14:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/734628"], "description"=>"<p>Proportion of information transferred, IT, for each phonetic feature (information received divided by information transmitted) for the VCVs presented in noise.</p>", "links"=>[], "tags"=>["analyses"], "article_id"=>404995, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.t003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_analyses_for_speech_in_noise_/404995", "title"=>"Feature analyses for speech in noise.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-19 01:23:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/734261"], "description"=>"<p>Results for moderate and severe degrees of smearing are in panels (a) and (b), respectively. Percent correct for unenhanced speech is represented along the abscissa and percent correct for contrast-enhanced speech is represented along the ordinate. The red dashed box represents the percent correct for the control speech (no smearing, no enhancement). The mean and standard errors for each condition are displayed on the graph. Asterisks indicate the significance level for paired t-tests [** for <i>p</i>≤0.01 and *** for <i>p</i>≤0.001].</p>", "links"=>[], "tags"=>["plots", "percent", "vcvs"], "article_id"=>404617, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plots_of_percent_correct_for_VCVs_in_quiet_/404617", "title"=>"Scatter plots of percent correct for VCVs in quiet.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:16:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/734184"], "description"=>"<p>(a) and (b) Spectrograms of the unenhanced and enhanced VCV /aga/ as spoken by an adult male talker. Vertical lines in the spectrograms correspond to time windows used in (c) and (d), which show spectra for the unenhanced (thick-solid line) and enhanced (thin-dotted line) signals from 16-ms time segments centered at 135 and 152 ms, respectively. (e) Spectral smearing of the unenhanced and enhanced time segments in (c) with a moderate degree of smearing (thick-solid and thin-dotted lines, respectively). (f) Formant peak locations of the stimuli shown in (a) and (b) as derived from linear predictive coding.</p>", "links"=>[], "tags"=>["enhancement", "spectral", "smearing"], "article_id"=>404538, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g003", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_how_contrast_enhancement_and_spectral_smearing_affect_the_speech_spectrum_/404538", "title"=>"Example of how contrast enhancement and spectral smearing affect the speech spectrum.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:15:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/734480"], "description"=>"<p>See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024630#pone-0024630-g004\" target=\"_blank\">Figure 4</a> legend.</p>", "links"=>[], "tags"=>["plots", "percent", "hvds", "db", "snr"], "article_id"=>404835, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plots_of_percent_correct_for_hVds_in_6_dB_SNR_noise_/404835", "title"=>"Scatter plots of percent correct for hVds in 6 dB SNR noise.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:20:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/734411"], "description"=>"<p>See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024630#pone-0024630-g004\" target=\"_blank\">Figure 4</a> legend. Asterisks indicate the significance level for paired t-tests [* for <i>p</i>≤0.05 and ** for <i>p</i>≤0.01].</p>", "links"=>[], "tags"=>["plots", "percent", "hvds"], "article_id"=>404770, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plots_of_percent_correct_for_hVds_in_quiet_/404770", "title"=>"Scatter plots of percent correct for hVds in quiet.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:19:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/734547"], "description"=>"<p>Scatter plots of percent correct for hVds in 0 dB SNR noise.</p>", "links"=>[], "tags"=>["plots", "percent", "hvds", "db", "snr"], "article_id"=>404911, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g008", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plots_of_percent_correct_for_hVds_in_0_dB_SNR_noise_/404911", "title"=>"Scatter plots of percent correct for hVds in 0 dB SNR noise.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:21:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/734655"], "description"=>"<p>Proportion of information transferred, IT, for each phonetic feature (information received divided by information transmitted) for the VCVs presented in quiet.</p>", "links"=>[], "tags"=>["analyses"], "article_id"=>405026, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_analyses_for_speech_in_quiet_/405026", "title"=>"Feature analyses for speech in quiet.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-19 01:23:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/734329"], "description"=>"<p>See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024630#pone-0024630-g004\" target=\"_blank\">Figure 4</a> legend.</p>", "links"=>[], "tags"=>["plots", "percent", "vcvs"], "article_id"=>404687, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g005", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plots_of_percent_correct_for_VCVs_in_noise_/404687", "title"=>"Scatter plots of percent correct for VCVs in noise.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:18:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/734696"], "description"=>"<p>For VCVs and hVds in quiet and in noise, outcomes for mixed-design ANOVAs with enhancement as the within-subjects variable and degree of smearing as the between-subjects variable. Asterisks indicate level of significance [* for <i>p</i>≤0.05, ** for <i>p</i>≤0.01, and *** for <i>p</i>≤0.001] and <i>N.S.</i> indicates a non-significant result.</p>", "links"=>[], "tags"=>["neuroscience", "physics", "otolaryngology"], "article_id"=>405059, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Anova_results_/405059", "title"=>"Anova results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-19 01:24:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/734019"], "description"=>"<p>The incoming signal, x(t), is first decomposed into 110.25-Hz wide frequency channels using polyphase decomposition (see text). Intermediate processing incorporates a winner-take-all (WTA) strategy in which channel gain is weighted in a way that simulates a lateral inhibitory network and sharpens spectral contrast. The output signal, y(t), is synthesized by an inverse of the process used to analyze the signal.</p>", "links"=>[], "tags"=>["enhancement"], "article_id"=>404377, "categories"=>["Physics", "Medicine", "Neuroscience"], "users"=>["Joshua M. Alexander", "Rick L. Jenison", "Keith R. Kluender"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024630.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_the_contrast_enhancement_algorithm_/404377", "title"=>"Schematic of the contrast enhancement algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-19 01:12:57"}

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Relative Metric

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