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4 results for “Lombard speech”
Mandarin matrix sentence test recordings: Lombard and plain speech with different speakers
<p>This dataset was recorded within the Deutsche Forschungsgemeinschaft (DFG) project: Experiments and models of speech recognition across tonal and non-tonal language systems (EMSATON, Projektnummer 415895050).</p> <p>The Lombard effect or Lombard reflex is the involuntary tendency of speakers to increase their vocal effort when speaking in loud noise to enhance the audibility of their voice. Up to date, the phenomena of Lombard effects were observed in different languages. The present database aimed at providing recordings for studying the Lombard effect with Mandarin speech.</p> <p>Eleven native-Mandarin talkers (6 female and 5 male) were recruited, both Lombard/plain speech were recorded from the same talker in the same day. </p> <p>All speakers produced fluent standard Mandarin speech (North China). All listeners were normal-hearing with pure tone thresholds of 20 dB hearing level or better at audiometric octave frequencies between 125 and 8000 Hz. All listeners provided written informed consent, approved by the Ethics Committee of Carl von Ossietzky University of Oldenburg. Listeners received an hourly compensation for their participation.</p> <p>The recording sentences were same as the official Mandarin Chinese matrix sentence test (<a href="#_ENREF_2">CMNmatrix, Hu et al. 2018</a>). </p> <p> One hundred sentences (ten base lists of ten sentences) of the CMNmatrix were recorded from each speaker in both plain and Lombard speaking styles (each base list containing all 50 words). The 100 sentences were divided into 10 blocks of 10 sentences each, and the plain and Lombard blocks were presented in an alternating order. The recording took place in a double-walled, sound-attenuated booth fulfilling ISO 8253-3 (ISO 8253-3, 2012), using a Neumann 184 microphone with a cardioid characteristic (Georg Neumann GmbH, Berlin, Germany) and a Fireface UC soundcard (with a sampling rate of 44100 Hz and resolution of 16 bits). The recording procedure generally followed the procedures of Alghamdi et al. (2018). A Mandarin-native speaker and a phonetician participated in the recording session and listened to the sentences to control the pronunciations, intonation, and speaking rate. During the recording, the speaker was instructed to read the sentence presented on a frontal screen. In case of any mispronunciation or change in the intonation, the speaker was asked via the screen to repeat the sentence again, and on average, each sentence was recorded twice. In Lombard conditions the speaker was regularly asked via a prompt to repeat a sentence, to keep the speaker in the Lombard communication situation. For the plain-speech recording blocks, the speakers were asked to pronounce the sentences with natural intonation and accentuation, and at an intermediate speaking rate, which was facilitated by a progress bar on the screen. Furthermore, the speakers were asked to keep the speaking effort constant and to avoid any exaggerated pronunciations that could lead to unnatural speech cues. For the Lombard speech recording blocks, speakers were instructed to imagine a conversation to another person in a pub-like situation. During the whole recording session, speakers wore headphones (Sennheiser HDA200) that provided the audio signal of the speaker.. In the Lombard condition, the stationary speech-shaped noise ICRA1 (Dreschler et al., 2001) was mixed with the speaker’s audio signal at a level of 80 dB SPL (calibrated with a Brüel & Kjær (B&K) 4153 artificial ear, a B&K 4134 0.5-inch inch microphone, a B&K 2669 preamplifier, and a B&K 2610). Previous studies showed that this level induced a robust Lombard speech without the danger of inducing hearing damage (Alghamdi et al., 2018).</p> <p>The sentences were cut from the recording, high-pass filtered (60 Hz cut-off frequency) and set to the average root-mean-square level of the original speech material of the Mandarin Matrix test (Hu et al., 2018). Then the best version of each sentence was chosen by native-Mandarin speakers regarding pronunciation, tempo, and intonation.</p> <p>For more detailed information, please contact hongmei.hu@uni-oldenburg, sabine.hochmuth@uni-oldenburg.de. </p> <p><em>Hu H, Xi X, Wong LLN, Hochmuth S, Warzybok A, Kollmeier B (2018) Construction and evaluation of the mandarin chinese matrix (cmnmatrix) sentence test for the assessment of speech recognition in noise. International Journal of Audiology 57:838-850. https://doi.org/10.1080/14992027.2018.1483083</em></p> <p> </p>
The Grid Audio-Visual Lombard Speech Corpus
<p>Lombard Grid is a bi-view audiovisual Lombard speech corpus which can be used to support joint computational/behavioral studies in speech perception. The corpus includes 54 talkers, with 100 utterances per talker (50 Lombard and 50 plain utterances). This dataset follows the same sentence format as the audiovisual <a href="https://asa.scitation.org/doi/10.1121/1.5042758">Grid corpus</a>, and can thus be considered as an extension of that corpus. The sentence sets used in the Lombard Grid corpus are unique, however, and have not been utilized by the Grid corpus.</p> <p>It offers two synchronised views of the talkers (front and side) to facilitate analysis of speech from different angles. A bespoke head-mounted camera system was used to collect both front and profile views of the talkers.</p> <p><strong>Statistics</strong>: 54 talkers: 30 female talkers and 24 male talkers; 5,400 (audio, front video and side video) utterances (16,200 files in total): 50% Lombard utterances, 50% plain reference utterances.</p> <p>The dataset is described in detail in the paper,</p> <p>Najwa Alghamdi, Steve Maddock, Ricard Marxer, Jon Barker and Guy J. Brown,, "A corpus of audio-visual Lombard speech with frontal and profile views", The Journal of the Acoustical Society of America 143, El523 (2018) </p> <p>The paper is available online at <a href="http://eprints.whiterose.ac.uk/131924/">White Rose Online Research</a>.</p> <p>------------------------------------------------------------------------------------</p> <p><strong>Notes on Filenaming</strong></p> <p><strong>Filename format</strong></p> <p>SPKR_COND_UTTERANCE.wav|.mov - e.g., s8_p_sbbi9p.wav</p> <p>*SPKR = s1 to s55</p> <p>*COND = l or p, where l=> Lombard, p=> plain (i.e. non-Lombard)</p> <p>*UTTERANCE = 6-character Grid utterance code, e.g. 'pgag6a' which means 'place green at g 6 again'</p> <p><strong>Metadata format</strong></p> <p>*SPKR = s1 to s55</p> <p>*SESSION = 1 or 2</p> <p>*INDEX = 1 to 10 for ordering of the recording blocks</p> <p>*SUBINDEX = 1 to 10 for ordering of utterance in a 10-utterance block.</p> <p>*COND = l or r, where l=> Lombard, p=> plain (i.e. non-Lombard)</p> <p>*UTTERANCE = 6-character Grid utterance code, e.g. 'pgag6a' which means 'place green at g 6 again'</p> <p>If a sentence is spoken incorrectly then the filename will be</p> <p>_WRONG.wav e.g. s8_2_38_8_r_lrwizp_WRONG_lrbizp.wav</p> <p>*TRANS = the Grid utterance code for what was actually said.</p>
Lombard speech database for German language
<p>This is a publication of Lombard speech database for German language. Additionally, a GitHub project has been created where information about database updates and related data will be stored:</p> <p>https://github.com/Telecommunication-Telemedia-Assessment/Lombard-Speech-database.git</p> <p>For citations please use:</p> <p>Sołoducha et al., "Lombard speech database for German Language", Proc. of German Annual Conference on Acoustics (DAGA), 2016</p>
The Berlin Dataset of Lombard and Masked Speech (BELMASK)
<p>The Berlin Dataset of Lombard and Masked Speech (BELMASK) is a phonetically controlled audiovisual dataset of speech produced in adverse speaking conditions. The dataset contains in total 128 min of audio and video recordings of 10 German native speakers (4 female, 6 male) with a mean age of 30.2 years (SD: 6.3 years), uttering matrix sentences in cued, uninstructed speech in four conditions: (i) with a Filtering Facepiece P2 (FFP2), (ii) without an FFP2 mask in silence, (iii) with an FFP2 mask while exposed to noise, iv) without an FFP2 mask while exposed to noise. Noise consisted of mixed-gender six-talker babble played over headphones to the speakers, triggering the Lombard effect. All conditions are readily available in face-and-voice and voice-only formats. The speech material is annotated, employing a multi-layer architecture, and was originally conceptualized to be used for the administration of a working memory task. The dataset is available for academic research in the area of speech communication, acoustics, psychology and related disciplines upon request, <strong>after signing an End User License Agreement (EULA)</strong>.</p>
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