Phonetic Temporal Neural Model for Language Identification

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.Comment: Submitted to TASL

DEVELOPMENT OF HIGH-PERFORMANCE AND LARGE-SCALE VIETNAMESE AUTOMATIC SPEECH RECOGNITION SYSTEMS

Automatic Speech Recognition (ASR) systems convert human speech into the corresponding transcription automatically. They have a wide range of applications such as controlling robots, call center analytics, voice chatbot. Recent studies on ASR for English have achieved the performance that surpasses human ability. The systems were trained on a large amount of training data and performed well under many environments. With regards to Vietnamese, there have been many studies on improving the performance of existing ASR systems, however, many of them are conducted on a small-scaled data, which does not reflect realistic scenarios. Although the corpora used to train the system were carefully design to maintain phonetic balance properties, efforts in collecting them at a large-scale are still limited. Specifically, only a certain accent of Vietnam was evaluated in existing works. In this paper, we first describe our efforts in collecting a large data set that covers all 3 major accents of Vietnam located in the Northern, Center, and Southern regions. Then, we detail our ASR system development procedure utilizing the collected data set and evaluating different model architectures to find the best structure for Vietnamese. In the VLSP 2018 challenge, our system achieved the best performance with 6.5% WER and on our internal test set with more than 10 hours of speech collected real environments, the system also performs well with 11% WE

Topic Identification for Speech without ASR

Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs. However, under resource-limited conditions, the manually transcribed speech required to develop standard ASR systems can be severely limited or unavailable. In this paper, we investigate alternative unsupervised solutions to obtaining tokenizations of speech in terms of a vocabulary of automatically discovered word-like or phoneme-like units, without depending on the supervised training of ASR systems. Moreover, using automatic phoneme-like tokenizations, we demonstrate that a convolutional neural network based framework for learning spoken document representations provides competitive performance compared to a standard bag-of-words representation, as evidenced by comprehensive topic ID evaluations on both single-label and multi-label classification tasks.Comment: 5 pages, 2 figures; accepted for publication at Interspeech 201

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13636-015-0063-8Spoken term detection (STD) aims at retrieving data from a speech repository given a textual representation of the search term. Nowadays, it is receiving much interest due to the large volume of multimedia information. STD differs from automatic speech recognition (ASR) in that ASR is interested in all the terms/words that appear in the speech data, whereas STD focuses on a selected list of search terms that must be detected within the speech data. This paper presents the systems submitted to the STD ALBAYZIN 2014 evaluation, held as a part of the ALBAYZIN 2014 evaluation campaign within the context of the IberSPEECH 2014 conference. This is the first STD evaluation that deals with Spanish language. The evaluation consists of retrieving the speech files that contain the search terms, indicating their start and end times within the appropriate speech file, along with a score value that reflects the confidence given to the detection of the search term. The evaluation is conducted on a Spanish spontaneous speech database, which comprises a set of talks from workshops and amounts to about 7 h of speech. We present the database, the evaluation metrics, the systems submitted to the evaluation, the results, and a detailed discussion. Four different research groups took part in the evaluation. Evaluation results show reasonable performance for moderate out-of-vocabulary term rate. This paper compares the systems submitted to the evaluation and makes a deep analysis based on some search term properties (term length, in-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and in-language/foreign terms).This work has been partly supported by project CMC-V2 (TEC2012-37585-C02-01) from the Spanish Ministry of Economy and Competitiveness. This research was also funded by the European Regional Development Fund, the Galician Regional Government (GRC2014/024, “Consolidation of Research Units: AtlantTIC Project” CN2012/160)

Multilingual audio information management system based on semantic knowledge in complex environments

This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users.This work is being funded by Grants: TEC201677791-C4 from Plan Nacional de I + D + i, Ministry of Economic Affairs and Competitiveness of Spain and from the DomusVi Foundation Kms para recorder, the Basque Government (ELKARTEK KK-2018/00114, GEJ IT1189-19, the Government of Gipuzkoa (DG18/14 DG17/16), UPV/EHU (GIU19/090), COST ACTION (CA18106, CA15225)

Adaptation and Augmentation: Towards Better Rescoring Strategies for Automatic Speech Recognition and Spoken Term Detection

Selecting the best prediction from a set of candidates is an essential problem for many spoken language processing tasks, including automatic speech recognition (ASR) and spoken keyword spotting (KWS). Generally, the selection is determined by a confidence score assigned to each candidate. Calibrating these confidence scores (i.e., rescoring them) could make better selections and improve the system performance. This dissertation focuses on using tailored language models to rescore ASR hypotheses as well as keyword search results for ASR-based KWS. This dissertation introduces three kinds of rescoring techniques: (1) Freezing most model parameters while fine-tuning the output layer in order to adapt neural network language models (NNLMs) from the written domain to the spoken domain. Experiments on a large-scale Italian corpus show a 30.2% relative reduction in perplexity at the word-cluster level and a 2.3% relative reduction in WER in a state-of-the-art Italian ASR system. (2) Incorporating source application information associated with speech queries. By exploring a range of adaptation model architectures, we achieve a 21.3% relative reduction in perplexity compared to a fine-tuned baseline. Initial experiments using a state-of-the-art Italian ASR system show a 3.0% relative reduction in WER on top of an unadapted 5-gram LM. In addition, human evaluations show significant improvements by using the source application information. (3) Marrying machine learning algorithms (classification and ranking) with a variety of signals to rescore keyword search results in the context of KWS for low-resource languages. These systems, built for the IARPA BABEL Program, enhance search performance in terms of maximum term-weighted value (MTWV) across six different low-resource languages: Vietnamese, Tagalog, Pashto, Turkish, Zulu and Tamil

Hand gesture recognition using Kinect.

Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this thesis. a novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HCR system is implemented with Microsoft Visual Studio 2010. Two different scenarios for HGR are provided: the Popular Gesture with nine gestures, and the Numbers with nine gestures. The system allows the users to select a scenario, and it is able to detect hand gestures made by users. to identify fingers, and to recognize the meanings of gestures, and to display the meanings and pictures on screen. The accuracy of the HGR system is from 84% to 99% with single hand gestures, and from 90% to 100% if both hands perform the same gesture at the same time. Because the depth sensor of Kinect is an infrared camera, the lighting conditions. signers\u27 skin colors and clothing, and background have little impact on the performance of this system. The accuracy and the robustness make this system a versatile component that can be integrated in a variety of applications in daily life