The Spoken Web Search Task

International audienceIn this paper, we describe the "Spoken Web Search" Task, which is being held as part of the 2012 MediaEval campaign. The purpose of this task is to perform audio search in multiple languages, with very little resources being available for each individual language. The data is being taken from audio content that was created in live and realistic low-resource settings

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

ELIRF at MEDIAEVAL 2013: Spoken Web Search Task

In this paper, we present the systems that the Natural Language Engineering and Pattern Recognition group (ELiRF) has submitted to the MediaEval 2013 Spoken Web Search task. All of them are based on a Subsequence Dynamic Time Warping algorithm and are zero-resources systems.Work funded by the Spanish Government and the E.U. under contract TIN2011-28169-C05 and FPU Grant AP2010- 4193.Gómez Adrian, JA.; Hurtado Oliver, LF.; Calvo Lance, M.; Sanchís Arnal, E. (2013). ELIRF at MEDIAEVAL 2013: Spoken Web Search Task. CEUR Workshop Proceedings. 1042:59-60. http://hdl.handle.net/10251/38157S5960104

A phonetic-based approach to query-by-example spoken term detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-41822-8_63Query-by-Example Spoken Term Detection (QbE-STD) tasks are usually addressed by representing speech signals as a sequence of feature vectors by means of a parametrization step, and then using a pattern matching technique to find the candidate detections. In this paper, we propose a phoneme-based approach in which the acoustic frames are first converted into vectors representing the a posteriori probabilities for every phoneme. This strategy is specially useful when the language of the task is a priori known. Then, we show how this representation can be used for QbE-STD using both a Segmental Dynamic Time Warping algorithm and a graph-based method. The proposed approach has been evaluated with a QbE-STD task in Spanish, and the results show that it can be an adequate strategy for tackling this kind of problemsWork partially supported by the Spanish Ministerio de Economía y Competitividad under contract TIN2011-28169-C05-01 and FPU Grant AP2010-4193, and by the Vic. d’Investigació of the UPV (PAID-06-10)Hurtado Oliver, LF.; Calvo Lance, M.; Gómez Adrian, JA.; García Granada, F.; Sanchís Arnal, E. (2013). A phonetic-based approach to query-by-example spoken term detection. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 8529:504-511. https://doi.org/10.1007/978-3-642-41822-8_63S5045118529Anguera, X., Macrae, R., Oliver, N.: Partial sequence matching using an unbounded dynamic time warping algorithm. In: ICASSP, pp. 3582–3585 (2010)Hazen, T., Shen, W., White, C.: Query-by-example spoken term detection using phonetic posteriorgram templates. In: ASRU, pp. 421–426 (2009)Zhang, Y., Glass, J.: Unsupervised spoken keyword spotting via segmental DTW on gaussian posteriorgrams. In: ASRU, pp. 398–403 (2009)Akbacak, M., Vergyri, D., Stolcke, A.: Open-vocabulary spoken term detection using graphone-based hybrid recognition systems. In: ICASSP, pp. 5240–5243 (2008)Fiscus, J.G., Ajot, J., Garofolo, J.S., Doddingtion, G.: Results of the 2006 spoken term detection evaluation. In: Proceedings of ACM SIGIR Workshop on Searching Spontaneous Conversational, pp. 51–55 (2007)Metze, F., Barnard, E., Davel, M., Van Heerden, C., Anguera, X., Gravier, G., Rajput, N., et al.: The spoken web search task. In: Working Notes Proceedings of the MediaEval 2012 Workshop (2012)Gómez, J.A., Castro, M.J.: Automatic segmentation of speech at the phonetic level. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SSPR & SPR 2002. LNCS, vol. 2396, pp. 672–680. Springer, Heidelberg (2002)Gómez, J.A., Sanchis, E., Castro-Bleda, M.J.: Automatic speech segmentation based on acoustical clustering. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR & SPR 2010. LNCS, vol. 6218, pp. 540–548. Springer, Heidelberg (2010)Moreno, A., Poch, D., Bonafonte, A., Lleida, E., Llisterri, J., Marino, J., Nadeu, C.: Albayzin speech database: Design of the phonetic corpus. In: Third European Conference on Speech Communication and Technology (1993)Park, A., Glass, J.: Towards unsupervised pattern discovery in speech. In: ASRU, pp. 53–58 (2005)Kullback, S.: Information theory and statistics. Courier Dover Publications (1997)MAVIR corpus, http://www.lllf.uam.es/ESP/CorpusMavir.htm

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

Using Zero-Resource Spoken Term Discovery for Ranked Retrieval

Research on ranked retrieval of spoken con-tent has assumed the existence of some auto-mated (word or phonetic) transcription. Re-cently, however, methods have been demon-strated for matching spoken terms to spoken content without the need for language-tuned transcription. This paper describes the first application of such techniques to ranked re-trieval, evaluated using a newly created test collection. Both the queries and the collection to be searched are based on Gujarati produced naturally by native speakers; relevance assess-ment was performed by other native speak-ers of Gujarati. Ranked retrieval is based on fast acoustic matching that identifies a deeply nested set of matching speech regions, cou-pled with ways of combining evidence from those matching regions. Results indicate that the resulting ranked lists may be useful for some practical similarity-based ranking tasks.