Hours Of Speech Data Research Articles

Speech data collection system for KUI, a Low resourced tribal language

<p>A new generation of speech translation technology is being developed to enable natural cross-language communication. Research efforts must focus on large vocabulary, spontaneous speech, and speaker variances to accommodate the varying demands of speech recognition technologies. These are important issues that need to be resolved for the general application of voice recognition in realistic settings. Most languages with limited resources don’t even have any speech data. Creating speech corpora is extremely difficult and time-consuming. Among all, KUI is regarded as one of the low-resource languages. In this paper, we developed the speech dataset for the KUI language to document and preserve their culture, tradition, and history for future generations. We also discuss the design, data collection procedures, and implementations and outline the different research possibilities using our KUI dataset. This paper mainly describes the GUI and method for the collection of KUI speech more quickly. In this section, the statistics of the people who helped and contributed to the collection of this KUI dataset have been provided. This study details a novel method of gathering data for any speech dataset. Using this process, we collected 60 hours of speech data sampled at 16 kHz by three different devices such as a Zoom recorder, Mobile, and Laptop from 80 different speakers. Each speaker contributed 500 sentences in the KUI language. A GUI application is designed to capture the speeches of numerous speakers in the KUI language. Several guidelines are proposed and used for the collection of the KUI speech dataset. All the guidelines are based on real-time experience gained during the data collection process by our team members.</p>

Investigating the feasibility of harvesting broadcast speech data to develop resources for South African languages

Sufficient target language data remains an important factor in the development of automatic speech recognition (ASR) systems. For instance, the substantial improvement in acoustic modelling that deep architectures have recently achieved for well-resourced languages requires vast amounts of speech data. Moreover, the acoustic models in state-of-the-art ASR systems that generalise well across different domains are usually trained on various corpora, not just one or two. Diverse corpora containing hundreds of hours of speech data are not available for resource limited languages. In this paper, we investigate the feasibility of creating additional speech resources for the official languages of South Africa by employing a semi-automatic data harvesting procedure. Factorised time-delay neural network models were used to generate phone-level transcriptions of speech data harvested from different domains.

Formosa Speech in the Wild Corpus for Improving Taiwanese Mandarin Speech-Enabled Human-Computer Interaction

Mandarin in Taiwan is notably different from other variants of Mandarin in terms of lexical use and accents. However, from an investment perspective, it remains debated whether the general-purpose Mandarin speech recognition (MSR) systems are sufficient for supporting human-computer interaction in Taiwan. In addressing this question, we established the Formosa (an ancient name of Taiwan given by the Portuguese) Speech in the Wild (FSW) (Liao 2018) project to (1) collect large-scale Taiwanese Mandarin speech to boost Taiwanese-specific MSR technique development, and (2) host a Formosa Speech Recognition (FSR) challenge (Liao 2018) to promote the corpus as well as to evaluate the performance of the available Taiwanese-specific MSR systems. The FSW project has focused on transcribing spontaneous Taiwanese Mandarin speech selected from real-life, multi-genre broadcast radio speech provided by Taiwan’s National Education Radio (2018). We plan to publicly release about 3000 hours of speech data at the end of 2019. FSR-2018 (Liao 2018) was the culmination of FSW’s events in the year 2018, which featured a Taiwanese broadcast Mandarin speech recognition evaluation campaign using released corpora. The challenge was also an official activity (Liao 2018) of the 11th International Symposium on Chinese Spoken Language Processing (ISCSLP) [22]. At the end of 2018, the first 4 volumes of the FSW Corpus, NER-Trs-Vol1∼4, a total of 610.2 hours of speech data, were released to support two events, Formosa Grand Challenge, Talk to AI (FGC) (Ministry of Science And Technology Taiwan 2018) (Dec. 2017 ∼ Mar. 2019) and FSR-2018 challenge (Liao 2018) (June 2018 ∼ Nov. 2018), which had 147 and 27 participating teams respectively. For FSR-2018, 30 recognition results on the final-test set were submitted by 16 teams. The evaluation results revealed that the best Taiwanese-specific MSR system achieved an 8.1% Chinese character error rate (CER). As reference, the performances of iFlyTek’s (ISCSLP 2018) and Google’s (2018) commercial MSR systems which were not optimized for this task were 18.8% and 20.6% CERs, respectively. Taken together, we argued that a Taiwanese-specific MSR system is necessary for improving the performance of Taiwanese Mandarin speech-enabled human-computer interaction.

UTDallas-PLTL:Advancing multi-stream speech processing for interaction assessment in peer-led team learning

Robust speech processing for single-stream audio data has achieved significant progress in the last decade. However, multi-stream speech processing poses new challenges not present in single-stream data. The peer-led team learning (PLTL) is a teaching paradigm popular among US universities for undergraduate education in STEM courses. In collaboration with UTDallas Student Success Center, we collected CRSS-PLTL and CRSS-PLTL-II corpora for assessment of speech communications in PLTL sessions. Both corpora consist of longitudinal recordings of five teams studying undergraduate Chemistry and Calculus courses consisting of 300 hours of speech data. The multi-stream audio data has unique challenges: (i) time-synchronization; (ii) multi-stream speech processing for speech activity detection, speaker diarization and linking, speech recognition, and (iii) behavioral informatics. We used a 1 kHz tone at the start and end of each session for time-synchronization of multi-stream audio. We leveraged auto-encoder neural network for combining MFCC features from multiple streams into compact bottleneck features. After diarization, each speaker segment is analyzed for behavioral metrics such as (i) dominance; (ii) curiosity in terms of question inflections; (iii) speech rate; (iv) cohesion; and (v) turn-duration and turn-taking patterns. Results are presented on individual and team based conversational interactions. This research suggests new emerging opportunities for wearable speech systems in education research.

Across-task variability in agrammatic performance

Background: As agrammatism is, at least partly, an adaptive behaviour, we investigated how compensation strategies manifest themselves in agrammatic performance. Aims: Within a functional theoretical framework of language use we conducted an in-depth exploration of across-task variability in agrammatic patients' oral production, submitting the morphosyntactic properties of their utterances to quantitative and qualitative analyses. Methods & Procedures: We designed an original data collection protocol comprising four tasks with increasing situational constraints (gradual manipulation of two external factors: use of instructions and presence or absence of pictures), in order to prompt the production of (i) spontaneous speech, (ii) narrative speech, (iii) descriptive speech and, finally, (iv) isolated sentences. We administered the tasks to six French-speaking agrammatic patients and nine normal controls, yielding the equivalent of 9 hours of speech data. We then conducted a multi-level and fine-grained analysis of the agrammatic and control corpora to assess oral production, entailing both morphological (open- vs closed-class word distribution, frequency of determiners, verb inflections) and syntactic aspects (sententials, non-sententials, well-formedness). Outcomes & Results: Results revealed across-task variability, suggesting that participants tended to adjust the morphosyntactic aspects of their speech according to task-dependent factors. Moreover, trade-offs were observed between morphosyntactic accuracy and oral fluency (i.e., speech rate), further pointing to the agrammatic patients' ability to gradually vary grammatical accuracy according to task constraints, rather than as a function of the limited online processing resources available to them. Results showed that agrammatic speakers used a variety of strategies to improve or reduce their grammatical accuracy according to task constraints. Conclusions: Agrammatic speakers rely excessively on ellipsis in spontaneous speech, and on corrective or monitoring strategies in elicited speech. Thus adaptation strategies vary from one task to another, depending on the type of speech to be produced (connected vs disconnected) and monitoring factors (attention allocated to formal encoding). Finally this study confirms the usefulness of functional and compensation-oriented therapies in aiding recovery from agrammatic aphasia.

Speech database development at MIT: Timit and beyond

Automatic speech recognition by computers can provide the most natural and efficient method of communication between humans and computers. While in recent years high performance speech recognition systems are beginning to emerge from research institutions, scientists unequivocally agree that the deployment of speech recognition systems into realistic operating environments will require many hours of speech data to help us model the inherent variability in the speech signal. This paper describes the experiences of researchers at MIT in the collection of two large speech databases which have somewhat complementary objectives. The timit database was designed to be task and speaker-independent, and is suitable for general acoustic-phonetic research. The voyager database, on the other hand, was intended for development and evaluation of a system which incorporates both speech and natural language processing. This database is particularly valuable as a source of spontaneous utterances elicited in a realistic goal-oriented environment.