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We propose to use a feature representation obtained by pairwise learning in a low-resource language for query-by-example spoken term detection (QbE-STD). We assume that word pairs identified by humans are available in the low-resource target language. The word pairs are parameterized by a multi-lingual bottleneck feature (BNF) extractor that is trained using transcribed data in high-resource languages...
Data mining has a great potential in different areas of health informatics. Data mining in health industry can minimize the health cost as well as reduces the risk of life by informing a person at initial stage. An automatic classification system capable of mining pathological data may contribute in health informatics significantly. In this paper, an automatic system to differentiate between pathological...
I-vector adaptation of DNN-HMM acoustic models has shown clear performance improvement for speech recognition. In this paper, we study this technique on Babel task. we use Swahili as target language (training data of 50 hours) and another 6 languages as multilingual resources to train i-vector extractors respectively. Our study shows that i-vector extractors trained with more multilingual data only...
Effective retrieval of multimodal data involves performing accurate segmentation and analysis of such data. With easy access to a number of audio and video sharing platforms online, user-generated content with considerably less than ideal recording conditions has increased rapidly. One major issue with such content is the presence of semantically irrelevant segments in such recordings. This leads...
Extraction of bilingual audio and text data is crucial for designing Speech to Speech (S2S) systems. In this work, we propose an automatic method to segment multilingual audio streams from movies. In addition, the audio streams are aligned with the corresponding subtitles. We found that the proposed method gives 89% perfectly segmented bilingual audio and 6% partially segmented bilingual audio. In...
In this paper, we present our experiments on the selection of basic phonetic units for the Vietnamese large vocabulary continuous speech recognition (LVCSR). Two acoustic models were compared. The first model has just used vowels or monophthongs as phonemes while the second one, which was proposed in this paper, has explored the use of diphthongs and triphthongs as phonemes as well. The two models...
We apply Hidden Conditional Random Fields (HCRFs) to the task of TIMIT phone recognition. HCRFs are discriminatively trained sequence models that augment conditional random fields with hidden states that are capable of representing subphones and mixture components. We extend HCRFs, which had previously only been applied to phone classification with known boundaries, to recognize continuous phone sequences...
Frequent pronunciation errors made by L2 learners of Dutch often concern vowel substitutions. To detect such pronunciation errors, ASR-based confidence measures (CMs) are generally used. In the current paper we compare and combine confidence measures with MFCCs and phonetic features. The results show that the best results are obtained by using MFCCs, then CMs, and finally phonetic features, and that...
Vehicle classification is an important task for various traffic monitoring applications. This paper investigates the capabilities of acoustic feature generation for vehicle classification. Six temporal and spectral features are extracted from the audio recordings. Six different classification algorithms are compared using the extracted features. We focus on a single sensor setting to keep the computational...
We present a model-based approach to separating and transcribing single-channel, multi-instrument polyphonic music in a semi-blind fashion. Our system extends the non-negative matrix factorization (NMF) algorithm to incorporate constraints on the basis vectors of the solution. In the context of music transcription, this allows us to encode prior knowledge about the space of possible instrument models...
The output of a speech recognition system is a stream of text features that is overlayed by noise resulting from errors in the system's statistical classification of the audio input. Conditional random fields (CRFs), which have already proven themselves to be efficient, high-performance named entity recognizers (NERs) for named entities from text, offer the promise to compensate part of these errors...
More and more efforts have been made for the research of emotional speech recently. Although we may, sometimes be able to make a definite perceptual decision on emotion state, emotion is actually a kind of cline in a large vector space. Different emotions can be thought of as zones along an emotional vector. To resolve the ambiguity of emotion perception, the authors make an array of perception experiments...
We developed a system that detects abnormal sound from sound signal observed by a surveillance microphone. Our system learns the ldquonormal soundrdquo from observation of the microphone, and then detects sounds never observed before as ldquoabnormal sounds.rdquo To this end, we developed a technique that uses multiple GMMs for modeling different levels of sound events efficiently. We also consider...
This paper describes the design and construction of the LOTUS-BN corpus, a Thai television broadcast news corpus. In addition to audio recordings and their transcription, this corpus also includes a detailed annotation of many interesting characteristics of broadcast news data such as acoustic condition, overlapping speech, news topic and named entity. The LOTUS-BN is still an ongoing project with...
Phonetically rich speech corpora play a pivotal role in speech research. The significance of such resources becomes crucial in the development of Automatic Speech Recognition systems and Text to Speech systems. This paper presents details of designing and developing an optimal context based phonetically rich speech corpus for Urdu that will serve as a baseline model for training a Large Vocabulary...
Speech recognition systems are usually trained using tremendous transcribed samples, and training data preparation is intensively time-consuming and costly. Aiming at achieving better performance of acoustic model with less transcribed samples, active learning is adopted in acoustic model training to iteratively select the most informative samples corresponding to some sample selection method. And...
This paper suggests an alternative solution for the task of spoken document retrieval (SDR). The proposed system runs retrieval on multi-level transcriptions (word and phone) produced by word and phone recognizers respectively, and their outputs are combined. We propose to use latent Dirichlet allocation (LDA) model for capturing the semantic information on word transcription. The LDA model is employed...
In this paper, we propose to use artificial neural networks (ANN) for voice conversion. We have exploited the mapping abilities of ANN to perform mapping of spectral features of a source speaker to that of a target speaker. A comparative study of voice conversion using ANN and the state-of-the-art Gaussian mixture model (GMM) is conducted. The results of voice conversion evaluated using subjective...
Training accurate acoustic models typically requires a large amount of transcribed data, which can be expensive to obtain. In this paper, we describe a novel semi-supervised learning algorithm for automatic speech recognition. The algorithm determines whether a hypothesized transcription should be used in the training by taking into consideration collective information from all utterances available...
Past work has produced fairly accurate automatic pitch-accent detectors, but it has often been noted that the accent class of a word is highly dependent on word identity, with some words and word types usually being accented and others not. We argue that a good accent detector should not only have high overall accuracy, but also be able to distinguish between accented and unaccented variants of the...
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