Imperfect Transcripts Research Articles

ALISA: An automatic lightly supervised speech segmentation and alignment tool

This paper describes the ALISA tool, which implements a lightly supervised method for sentence-level alignment of speech with imperfect transcripts. Its intended use is to enable the creation of new speech corpora from a multitude of resources in a language-independent fashion, thus avoiding the need to record or transcribe speech data. The method is designed so that it requires minimum user intervention and expert knowledge, and it is able to align data in languages which employ alphabetic scripts. It comprises a GMM-based voice activity detector and a highly constrained grapheme-based speech aligner. The method is evaluated objectively against a gold standard segmentation and transcription, as well as subjectively through building and testing speech synthesis systems from the retrieved data. Results show that on average, 70% of the original data is correctly aligned, with a word error rate of less than 0.5%. In one case, subjective listening tests show a statistically significant preference for voices built on the gold transcript, but this is small and in other tests, no statistically significant differences between the systems built from the fully supervised training data and the one which uses the proposed method are found.

Latent Semantic Analysis for Multimodal User Input With Speech and Gestures

This paper describes our work in semantic interpretation of a “multimodal language” with speech and gestures using latent semantic analysis (LSA). Our aim is to infer the domain-specific informational goal of multimodal inputs. The informational goal is characterized by lexical terms used in the spoken modality, partial semantics of gestures in the pen modality, as well as term co-occurrence patterns across modalities, leading to “multimodal terms.” We designed and collected a multimodal corpus of navigational inquiries. We also obtained perfect (i.e. manual) and imperfect (i.e. automatic via recognition) transcriptions for these. We automatically align parsed spoken locative references (SLRs) with their corresponding pen gesture(s) using the Viterbi alignment, according to their numeric and location type features. Then, we characterize each cross-modal integration pattern as a 3-tuple multimodal term with SLR, pen gesture type and their temporal relationship. We propose to use latent semantic analysis (LSA) to derive the latent semantics from manual (i.e. perfect) and automatic (i.e. imperfect) transcriptions of the collected multimodal inputs. In order to achieve this, both multimodal and lexical terms are used to compose an inquiry-term matrix, which is then factorized using singular value decomposition (SVD) to derive the latent semantics automatically. Informational goal inference based on the latent semantics shows that the informational goal inference accuracy of a disjoint test set is 99% and 84% when a perfect and imperfect projection model is used respectively, which performs significantly better than (at least 9.9% absolute) the baseline performance using vector-space model (VSM).

Automatic Parliamentary Meeting Minute Generation Using Rhetorical Structure Modeling

In this paper, we propose a one step rhetorical structure parsing, chunking and extractive summarization approach to automatically generate meeting minutes from parliamentary speech using acoustic and lexical features. We investigate how to use lexical features extracted from imperfect ASR transcriptions, together with acoustic features extracted from the speech itself, to form extractive summaries with the structure of meeting minutes. Each business item in the minute is modeled as a rhetorical chunk which consists of smaller rhetorical units. Principal Component Analysis (PCA) graphs of both acoustic and lexical features in meeting speech show clear self-clustering of speech utterances according to the underlying rhetorical state-for example acoustic and lexical feature vectors from the question and answer or motion of a parliamentary speech, are grouped together. We then propose a Conditional Random Fields (CRF)-based approach to perform both rhetorical structure modeling and extractive summarization in one step, by chunking, parsing and extraction of salient utterances. Extracted salient utterances are grouped under the labels of each rhetorical state, emulating meeting minutes to yield summaries that are more easily understandable by humans. We compare this approach to different machine learning methods. We show that our proposed CRF-based one step minute generation system obtains the best summarization performance both in terms of ROUGE-L F-measure at 74.5% and by human evaluation, at 77.5% on average.

Integrating imperfect transcripts into speech recognition systems for building high-quality corpora

Abstract The training of state-of-the-art automatic speech recognition (ASR) systems requires huge relevant training corpora. The cost of such databases is high and remains a major limitation for the development of speech-enabled applications in particular contexts (e.g. low-density languages or specialized domains). On the other hand, a large amount of data can be found in news prompts, movie subtitles or scripts, etc. The use of such data as training corpus could provide a low-cost solution to the acoustic model estimation problem. Unfortunately, prior transcripts are seldom exact with respect to the content of the speech signal, and suffer from a lack of temporal information. This paper tackles the issue of prompt-based speech corpora improvement, by addressing the problems mentioned above. We propose a method allowing to locate accurate transcript segments in speech signals and automatically correct errors or lack of transcript surrounding these segments. This method relies on a new decoding strategy where the search algorithm is driven by the imperfect transcription of the input utterances. The experiments are conducted on the French language, by using the ESTER database and a set of records (and associated prompts) from RTBF (Radio Télévision Belge Francophone). The results demonstrate the effectiveness of the proposed approach, in terms of both error correction and text-to-speech alignment.

Question answering from lecture videos based on an automatic semantic annotation

The number of digital lecture video recordings has increased dramatically. The accessibility, usability and the traceability of their content for students-use is limited. Therefore retrieval of audiovisual lecture recordings is a complex task. Speech recognition is applied to create a tentative and deficient transcription of the video recordings. The imperfect transcription is sufficient to generate semantic metadata serialized in an OWL file. A question answering system based on the automatically generated semantic annotations and a semantic search engine are presented. The annotation process is discussed, evaluated and compared to a perfectly annotated OWL file and, further, to a corrected transcript of the lecture.

Searching large collections of recorded speech: A preliminary study

AbstractThis paper reports on an exploratory study of the criteria searchers use when judging the relevance of recorded speech from radio programs and the attributes of a recording on which those judgments are based. Five volunteers each performed three searches using two systems (NPR Online and SpeechBot) for three questions and judged the relevance of the results. Data were collected through observation and screen capture, think aloud, and interviews; coded; and analyzed by looking for patterns. Criteria used as a basis for selection were found to be similar to those observed in relevance studies with printed materials, but the attributes used as a basis for assessing those criteria were found to exhibit modality‐specific characteristics. For example, audio replay was often found to be necessary when assessing story genre (e.g., report, interview, commentary) because of limitations in presently available metadata. Participants reported a strong preference for manually prepared summaries over passages extracted from automatic speech recognition transcripts, and consequential differences in search behavior were observed between the two conditions. Some important implications for interface and component design are drawn, such as the utility of summaries at multiple levels of detail in view of the difficulty of skimming imperfect transcripts and the potential utility of automatic speaker identification to support authority judgments in systems.