Speech Data Clause Samples
The 'Speech Data' clause defines the terms under which speech-related data—such as audio recordings, transcripts, or voice samples—are collected, used, and managed within the agreement. Typically, this clause outlines the types of speech data involved, the purposes for which the data may be processed (such as improving speech recognition technologies), and any restrictions on sharing or storing such data. Its core practical function is to ensure both parties understand their rights and obligations regarding speech data, thereby protecting privacy and clarifying data usage boundaries.
POPULAR SAMPLE Copied 1 times
Speech Data. To assess the labeling conventions of K-ToBI and to demonstrate that these conventions are applicable to various types of speech, we selected twenty utterances representing five different discourse types: TV drama, interview, news, text reading, and story reading. Four sentences were selected from each discourse type. These sentences contained a total of 153 words, and lasted a total of 78.5 seconds. 18 speakers (8 male and 10 female) produced the sentences. Table 1 shows a summary of the speech files: Twenty-one labelers, differing in their experience with intonation transcription and in their familiarity with the ToBI model, participated in the experiment. The labelers were divided into four groups: Group 1 (Experts), Group 2 (Familiar with K- ToBI), Group 3 (Familiar with the British intonation model, but new to K-ToBI and intonation transcription), and Group 4 (Beginners, completely new to any model of intonation or prosodic transcription). Each group included five labelers, except for Group 2 which had six labelers. The labelers came from four sites. Six of the labelers from Site A and all of the labelers from Site B and C were provided with 2-3 hours of lecture by the person in charge of each site during which the labeling conventions and background assumptions of K-ToBI were introduced. Site C had 4 hours of group discussion and a review session after the lecture. Two of the labelers from Site A and the one labeler from Site D performed their transcriptions based on the K-ToBI manual alone. Table 2 shows the distribution of labeler groups at each site. Sites # Experts (G1) # Familiar to K-ToBI (G2) # Familiar toother model (G3) # Beginners (G4) Discourse Types # of Utter- ances # of Words # of Speakers Total durat- ion (ms) interview 4 29 2 female 14,911 news 4 35 2 male, 2 female 16,869 reading 4 28 2 male, 2 female 15,849 story 4 30 2 male, 2 female 16,086 We selected 20 speech files from the data bank in Korea. The first author sent the following materials to the person in charge at each site: 1) the speech files in wave format, 2) the K-ToBI manual, version 3, together with the example sentence files mentioned in the manual, and 3) a Hangul file in which the sentences from each speech file were written in Hangul orthography with empty spaces below for writing tones and break indices. The wave files and the Hangul files for writing transcriptions were necessary because not all sites used the same speech analysis software (they ranged from xwaves ...
Speech Data. The audio recordings used were sourced from the JASMIN cor- pus [18]. The ▇▇▇▇▇▇ ▇▇▇▇▇▇ is a Dutch-Flemish corpus of read and extemporaneous speech collected from children, non- native speakers, and elderly people. Due to time restraints of the session in which the responses were collected, the ques- tionnaire was designed to be relatively short. Eight sentences were selected, each produced by a different non-native speaker of Dutch, aged between 11-17 years (Group 3 in the corpus). Second language (L2) learners were chosen as a reflection of the increasing diversity and multilingualism in society and schools. Furthermore, feedback on intelligibility is particularly benefi- cial for L2 learners. The sentences were extracted from recordings of children reading texts intended for L2 Dutch learners. The sentences were selected based on age-appropriateness for the listeners, in- telligibility, and the number and type of speech and/or reading errors, to produce a sample of sentences ranging in difficulty for each task. During selection the reader’s age, sex and read- ing level were also taken into account to try create a balanced sample. The sentences ranged from six to twelve words, except for one of twenty-one words (S5). 1▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇▇▇.▇▇▇/
Speech Data. If Client receives speech recognition services as part of the Epocrates EHR Service, Epocrates or its licensors may collect and use the audio files, associated transcriptions and log files provided by Authorized Users pursuant to this Agreement or generated in connection with the Epocrates EHR Service (“Speech Data”) to tune, enhance and improve the speech recognition and other components of the Epocrates EHR Service, and other services and products. In accepting the terms and conditions of this Agreement, Client acknowledges, consents and agrees that Epocrates and its licensors may collect the Speech Data as part of the Epocrates EHR Service and that such information shall only be used by Epocrates and its licensors, or third parties acting under the direction of Epocrates or its licensors pursuant to confidentiality agreements, to tune, enhance and improve the speech recognition and other components of the Epocrates EHR Service, and other services and products.
Speech Data. To assess the labeling conventions of K-ToBI and to demonstrate that these conventions are applicable to various types of speech, we selected twenty utterances representing five different discourse types: TV drama, interview, news, text reading, and story reading. Four sentences were selected from each discourse type. These sentences contained a total of 153 words, and lasted a total of 78.5 seconds. 18 speakers (8 male and 10 female) produced the sentences. Table 1 shows a summary of the speech files: Discourse Types # of Utter-ances # of Words # of Speakers Total durat- ion (ms) drama 4 31 2 male, 2 female 14,841 interview 4 29 2 female 14,911 news 4 35 2 male, 2 female 16,869 reading 4 28 2 male, 2 female 15,849 story 4 30 2 male, 2 female 16,086 Total 20 153 8 male, 10 female 78,556