352 research outputs found
Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity
This paper presents a new approach for unsupervised Spoken Term Detection
with spoken queries using multiple sets of acoustic patterns automatically
discovered from the target corpus. The different pattern HMM
configurations(number of states per model, number of distinct models, number of
Gaussians per state)form a three-dimensional model granularity space. Different
sets of acoustic patterns automatically discovered on different points properly
distributed over this three-dimensional space are complementary to one another,
thus can jointly capture the characteristics of the spoken terms. By
representing the spoken content and spoken query as sequences of acoustic
patterns, a series of approaches for matching the pattern index sequences while
considering the signal variations are developed. In this way, not only the
on-line computation load can be reduced, but the signal distributions caused by
different speakers and acoustic conditions can be reasonably taken care of. The
results indicate that this approach significantly outperformed the unsupervised
feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT
corpus.Comment: Accepted by ICASSP 201
Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data
It is well known that recognizers personalized to each user are much more
effective than user-independent recognizers. With the popularity of smartphones
today, although it is not difficult to collect a large set of audio data for
each user, it is difficult to transcribe it. However, it is now possible to
automatically discover acoustic tokens from unlabeled personal data in an
unsupervised way. We therefore propose a multi-task deep learning framework
called a phoneme-token deep neural network (PTDNN), jointly trained from
unsupervised acoustic tokens discovered from unlabeled data and very limited
transcribed data for personalized acoustic modeling. We term this scenario
"weakly supervised". The underlying intuition is that the high degree of
similarity between the HMM states of acoustic token models and phoneme models
may help them learn from each other in this multi-task learning framework.
Initial experiments performed over a personalized audio data set recorded from
Facebook posts demonstrated that very good improvements can be achieved in both
frame accuracy and word accuracy over popularly-considered baselines such as
fDLR, speaker code and lightly supervised adaptation. This approach complements
existing speaker adaptation approaches and can be used jointly with such
techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201
Intelligent system for spoken term detection using the belief combination
Spoken Term Detection (STD) can be considered as a sub-part of the automatic speech recognition which aims to extract the partial information from speech signals in the form of query utterances. A variety of STD techniques available in the literature employ a single source of evidence for the query utterance match/mismatch determination. In this manuscript, we develop an acoustic signal processing based approach for STD that incorporates a number of techniques for silence removal, dynamic noise filtration, and evidence combination using Dempster-Shafer Theory (DST). A ‘spectral-temporal features based voiced segment detection’ and ‘energy and zero cross rate based unvoiced segment detection’ are built to remove the silence segments in the speech signal. Comprehensive experiments have been performed on large speech datasets and consequently satisfactory results have been achieved with the proposed approach. Our approach improves the existing speaker dependent STD approaches, specifically the reliability of query utterance spotting by combining the evidences from multiple belief sources
Adaptive framing based similarity measurement between time warped speech signals using Kalman filter
Similarity measurement between speech signals aims at calculating the degree of similarity using acoustic features that has been receiving much interest due to the processing of large volume of multimedia information. However, dynamic properties of speech signals such as varying silence segments and time warping factor make it more challenging to measure the similarity between speech signals. This manuscript entails further extension of our research towards the adaptive framing based similarity measurement between speech signals using a Kalman filter. Silence removal is enhanced by integrating multiple features for voiced and unvoiced speech segments detection. The adaptive frame size measurement is improved by using the acceleration/deceleration phenomenon of object linear motion. A dominate feature set is used to represent the speech signals along with the pre-calculated model parameters that are set by the offline tuning of a Kalman filter. Performance is evaluated using additional datasets to evaluate the impact of the proposed model and silence removal approach on the time warped speech similarity measurement. Detailed statistical results are achieved indicating the overall accuracy improvement from 91 to 98% that proves the superiority of the extended approach on our previous research work towards the time warped continuous speech similarity measurement
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