Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Holistic Vocabulary Independent Spoken Term Detection

Within this thesis, we aim at designing a loosely coupled holistic system for Spoken Term Detection (STD) on heterogeneous German broadcast data in selected application scenarios. Starting from STD on the 1-best output of a word-based speech recognizer, we study the performance of several subword units for vocabulary independent STD on a linguistically and acoustically challenging German corpus. We explore the typical error sources in subword STD, and find that they differ from the error sources in word-based speech search. We select, extend and combine a set of state-of-the-art methods for error compensation in STD in order to explicitly merge the corresponding STD error spaces through anchor-based approximate lattice retrieval. Novel methods for STD result verification are proposed in order to increase retrieval precision by exploiting external knowledge at search time. Error-compensating methods for STD typically suffer from high response times on large scale databases, and we propose scalable approaches suitable for large corpora. Highest STD accuracy is obtained by combining anchor-based approximate retrieval from both syllable lattice ASR and syllabified word ASR into a hybrid STD system, and pruning the result list using external knowledge with hybrid contextual and anti-query verification.Die vorliegende Arbeit beschreibt ein lose gekoppeltes, ganzheitliches System zur Sprachsuche auf heterogenenen deutschen Sprachdaten in unterschiedlichen Anwendungsszenarien. Ausgehend von einer wortbasierten Sprachsuche auf dem Transkript eines aktuellen Wort-Erkenners werden zunächst unterschiedliche Subwort-Einheiten für die vokabularunabhängige Sprachsuche auf deutschen Daten untersucht. Auf dieser Basis werden die typischen Fehlerquellen in der Subwort-basierten Sprachsuche analysiert. Diese Fehlerquellen unterscheiden sich vom Fall der klassichen Suche im Worttranskript und müssen explizit adressiert werden. Die explizite Kompensation der unterschiedlichen Fehlerquellen erfolgt durch einen neuartigen hybriden Ansatz zur effizienten Ankerbasierten unscharfen Wortgraph-Suche. Darüber hinaus werden neuartige Methoden zur Verifikation von Suchergebnissen vorgestellt, die zur Suchzeit verfügbares externes Wissen einbeziehen. Alle vorgestellten Verfahren werden auf einem umfangreichen Satz von deutschen Fernsehdaten mit Fokus auf ausgewählte, repräsentative Einsatzszenarien evaluiert. Da Methoden zur Fehlerkompensation in der Sprachsuchforschung typischerweise zu hohen Laufzeiten bei der Suche in großen Archiven führen, werden insbesondere auch Szenarien mit sehr großen Datenmengen betrachtet. Die höchste Suchleistung für Archive mittlerer Größe wird durch eine unscharfe und Anker-basierte Suche auf einem hybriden Index aus Silben-Wortgraphen und silbifizierter Wort-Erkennung erreicht, bei der die Suchergebnisse mit hybrider Verifikation bereinigt werden

Spoken query processing for interactive information retrieval

It has long been recognised that interactivity improves the effectiveness of information retrieval systems. Speech is the most natural and interactive medium of communication and recent progress in speech recognition is making it possible to build systems that interact with the user via speech. However, given the typical length of queries submitted to information retrieval systems, it is easy to imagine that the effects of word recognition errors in spoken queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of classical information retrieval techniques for spoken query processing is robust to considerably high levels of word recognition errors, in particular for long queries. Moreover, in the case of short queries, both standard relevance feedback and pseudo relevance feedback can be effectively employed to improve the effectiveness of spoken query processing

Phoneme-based Video Indexing Using Phonetic Disparity Search

This dissertation presents and evaluates a method to the video indexing problem by investigating a categorization method that transcribes audio content through Automatic Speech Recognition (ASR) combined with Dynamic Contextualization (DC), Phonetic Disparity Search (PDS) and Metaphone indexation. The suggested approach applies genome pattern matching algorithms with computational summarization to build a database infrastructure that provides an indexed summary of the original audio content. PDS complements the contextual phoneme indexing approach by optimizing topic seek performance and accuracy in large video content structures. A prototype was established to translate news broadcast video into text and phonemes automatically by using ASR utterance conversions. Each phonetic utterance extraction was then categorized, converted to Metaphones, and stored in a repository with contextual topical information attached and indexed for posterior search analysis. Following the original design strategy, a custom parallel interface was built to measure the capabilities of dissimilar phonetic queries and provide an interface for result analysis. The postulated solution provides evidence of a superior topic matching when compared to traditional word and phoneme search methods. Experimental results demonstrate that PDS can be 3.7% better than the same phoneme query, Metaphone search proved to be 154.6% better than the same phoneme seek and 68.1 % better than the equivalent word search

Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (∼10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance. © 2013 IEEE

Low Resource Efficient Speech Retrieval

Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency. This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding. Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR