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.

Query-by-Example Spoken Term Detection ALBAYZIN 2012 evaluation: overview, systems, results and discussion

The final publication is available at Springer via http://dx.doi.org/10.1186/1687-4722-2013-23Query-by-Example Spoken Term Detection (QbE STD) aims at retrieving data from a speech data repository given an acoustic query containing the term of interest as input. Nowadays, it has been receiving much interest due to the high volume of information stored in audio or audiovisual format. QbE STD differs from automatic speech recognition (ASR) and keyword spotting (KWS)/spoken term detection (STD) since ASR is interested in all the terms/words that appear in the speech signal and KWS/STD relies on a textual transcription of the search term to retrieve the speech data. This paper presents the systems submitted to the ALBAYZIN 2012 QbE STD evaluation held as a part of ALBAYZIN 2012 evaluation campaign within the context of the IberSPEECH 2012 Conferencea. The evaluation consists of retrieving the speech files that contain the input queries, indicating their start and end timestamps within the appropriate speech file. Evaluation is conducted on a Spanish spontaneous speech database containing a set of talks from MAVIR workshopsb, which amount at about 7 h of speech in total. We present the database metric systems submitted along with all results and some discussion. Four different research groups took part in the evaluation. Evaluation results show the difficulty of this task and the limited performance indicates there is still a lot of room for improvement. The best result is achieved by a dynamic time warping-based search over Gaussian posteriorgrams/posterior phoneme probabilities. 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Unsupervised lexical clustering of speech segments using fixed dimensional acoustic embeddings

Unsupervised speech processing methods are essential for ap-plications ranging from zero-resource speech technology to modelling child language acquisition. One challenging prob-lem is discovering the word inventory of the language: the lexicon. Lexical clustering is the task of grouping unlabelled acoustic word tokens according to type. We propose a novel lexical clustering model: variable-length word segments are embedded in a fixed-dimensional acoustic space in which clustering is then performed. We evaluate several clustering algorithms and find that the best methods produce clusters with wide variation in sizes, as observed in natural language. The best probabilistic approach is an infinite Gaussian mix-ture model (IGMM), which automatically chooses the num-ber of clusters. Performance is comparable to that of non-probabilistic Chinese Whispers and average-linkage hierarchi-cal clustering. We conclude that IGMM clustering of fixed-dimensional embeddings holds promise as the lexical cluster-ing component in unsupervised speech processing systems. Index Terms — Lexical clustering, unsupervised learning, fixed-dimensional embeddings, lexical discovery