Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation

[Abstract] The huge amount of information stored in audio and video repositories makes search on speech (SoS) a priority area nowadays. Within SoS, Query-by-Example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given a spoken query. Research on this area is continuously fostered with the organization of QbE STD evaluations. This paper presents a multi-domain internationally open evaluation for QbE STD in Spanish. The evaluation aims at retrieving the speech files that contain the queries, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: MAVIR database, which comprises a set of talks from workshops; RTVE database, which includes broadcast television (TV) shows; and COREMAH database, which contains 2-people spontaneous speech conversations about different topics. The evaluation has been designed carefully so that several analyses of the main results can be carried out. We present the evaluation itself, the three databases, the evaluation metrics, the systems submitted to the evaluation, the results, and the detailed post-evaluation analyses based on some query properties (within-vocabulary/out-of-vocabulary queries, single-word/multi-word queries, and native/foreign queries). Fusion results of the primary systems submitted to the evaluation are also presented. Three different teams took part in the evaluation, and ten different systems were submitted. The results suggest that the QbE STD task is still in progress, and the performance of these systems is highly sensitive to changes in the data domain. Nevertheless, QbE STD strategies are able to outperform text-based STD in unseen data domains.Centro singular de investigación de Galicia; ED431G/04Universidad del País Vasco; GIU16/68Ministerio de Economía y Competitividad; TEC2015-68172-C2-1-PMinisterio de Ciencia, Innovación y Competitividad; RTI2018-098091-B-I00Xunta de Galicia; ED431G/0

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Soft margin estimation for automatic speech recognition

In this study, a new discriminative learning framework, called soft margin estimation (SME), is proposed for estimating the parameters of continuous density hidden Markov models (HMMs). The proposed method makes direct use of the successful ideas of margin in support vector machines to improve generalization capability and decision feedback learning in discriminative training to enhance model separation in classifier design. SME directly maximizes the separation of competing models to enhance the testing samples to approach a correct decision if the deviation from training samples is within a safe margin. Frame and utterance selections are integrated into a unified framework to select the training utterances and frames critical for discriminating competing models. SME offers a flexible and rigorous framework to facilitate the incorporation of new margin-based optimization criteria into HMMs training. The choice of various loss functions is illustrated and different kinds of separation measures are defined under a unified SME framework. SME is also shown to be able to jointly optimize feature extraction and HMMs. Both the generalized probabilistic descent algorithm and the Extended Baum-Welch algorithm are applied to solve SME. SME has demonstrated its great advantage over other discriminative training methods in several speech recognition tasks. Tested on the TIDIGITS digit recognition task, the proposed SME approach achieves a string accuracy of 99.61%, the best result ever reported in literature. On the 5k-word Wall Street Journal task, SME reduced the word error rate (WER) from 5.06% of MLE models to 3.81%, with relative 25% WER reduction. This is the first attempt to show the effectiveness of margin-based acoustic modeling for large vocabulary continuous speech recognition in a HMMs framework. The generalization of SME was also well demonstrated on the Aurora 2 robust speech recognition task, with around 30% relative WER reduction from the clean-trained baseline.Ph.D.Committee Chair: Dr. Chin-Hui Lee; Committee Member: Dr. Anthony Joseph Yezzi; Committee Member: Dr. Biing-Hwang (Fred) Juang; Committee Member: Dr. Mark Clements; Committee Member: Dr. Ming Yua

Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations

Query-by-example spoken term detection (QbE STD) aims at retrieving data from a speech repository given an acoustic query containing the term of interest as input. Nowadays, it is receiving much interest due to the large volume of multimedia information. This paper presents the systems submitted to the ALBAYZIN QbE STD 2014 evaluation held as a part of the ALBAYZIN 2014 Evaluation campaign within the context of the IberSPEECH 2014 conference. This is the second QbE STD evaluation in Spanish, which allows us to evaluate the progress in this technology for this language. The evaluation consists in retrieving the speech files that contain the input queries, indicating the start and end times where the input queries were found, along with a score value that reflects the confidence given to the detection of the query. Evaluation is conducted on a Spanish spontaneous speech database containing a set of talks from workshops, which amount to about 7 h of speech. We present the database, the evaluation metric, the systems submitted to the evaluation, the results, and compare this second evaluation with the first ALBAYZIN QbE STD evaluation held in 2012. Four different research groups took part in the evaluations held in 2012 and 2014. In 2014, new multi-word and foreign queries were added to the single-word and in-language queries used in 2012. Systems submitted to the second evaluation are hybrid systems which integrate letter transcription- and template matching-based systems. Despite the significant improvement obtained by the systems submitted to this second evaluation compared to those of the first evaluation, results still show the difficulty of this task and indicate that there is still room for improvement.This research was funded by the Spanish Government ('SpeechTech4All Project' TEC2012 38939 C03 01 and 'CMC-V2 Project' TEC2012 37585 C02 01), the Galician Government through the research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014) and 'AtlantTIC Project' CN2012/160, and also by the Spanish Government and the European Regional Development Fund (ERDF) under project TACTICA