Incorporating syllable duration into line-detection-based spoken term detection

Glass

IEEE/ACM Trans. Audio Speech Lang. Process.

et al. 2015

100

Spoken content retrieval refers to directly indexing and retrieving spoken content based on the audio rather than text descriptions. This potentially eliminates the requirement of producing text descriptions for multimedia content for indexing and retrieval purposes, and is able to precisely locate the exact time the desired information appears in the multimedia. Spoken content retrieval has been very successfully achieved with the basic approach of cascading automatic speech recognition (ASR) with text information retrieval: after the spoken content is transcribed into text or lattice format, a text retrieval engine searches over the ASR output to find desired information. This framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences.Index Terms-Spoken content retrieval, spoken term detection, query by example, semantic retrieval, joint optimization, pseudo-relevance feedback, graph-based random walk, unsupervised acoustic pattern discovery, query expansion, interactive retrieval, summarization, key term extraction.

Section: B Incorporating Prosodic Cuesmentioning

confidence: 99%

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Glass

IEEE/ACM Trans. Audio Speech Lang. Process.

et al. 2015

100

“…Segmenting speech into syllables helps in many speech applications such as speech recognition, synthesis and spoken term detection [1]. Detecting the syllable nuclei and the syllable boundary has been a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Syllable nucleus and boundary detection in noisy conditions

Patha¹,

Yegnanarayana²,

Gangashetty³

2016

Speech Prosody 2016

In this paper, utilizing the feature of the sonority, the problems of detecting the syllable nuclei and the syllable boundaries are explored. As there is minimal obstruction in the oral cavity in the production of sonorant sounds, sonority feature is used. A method is proposed for extracting the sonority profile from the strength of the dominant resonance frequency using the zero time windowing method. The strength of the dominant resonance frequencies in the specific band of 500 Hz to 1700 Hz is used. The proposed method is evaluated on SVL-DD-NEWScorpora. The results are compared with the Fourier transform method and the energy-based method of extracting the sonority profile. The proposed method performed better compared to other methods, especially in the presence of noise such as white, pink and babble. The method is also evaluated on TIMIT test database and the performance is on par with the current methods in detecting the syllable nuclei.

J AUDIO SPEECH MUSIC PROC.

“…However, the subword-based approach has the unique advantage that it can detect terms that consist of words that are not in the recognizer's vocabulary -out-of-vocabulary (OOV) terms -whereas the word-based approach can only detect in-vocabulary (INV) terms. Several subword unit types have been employed in the subwordbased approach, including word fragments [46], particles [47,48], acoustic words [49], graphones [6,7], multigrams [9,50], syllables [51][52][53], and graphemes [54], although phonemes are the most commonly used due to their simplicity and natural relationship with spoken languages [41,[55][56][57][58][59]. In order to exploit the relative advantages of the word and phoneme-based approaches, it has been proposed to combine these two approaches by using the word-based approach to detect INV terms and the subword-based approach to detect OOV terms, e.g., [41,56,[60][61][62][63][64].…”

Section: Introduction To Spoken Term Detection Technologymentioning

confidence: 99%

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

Tejedor

Toledano

López-Otero

et al. 2015

Spoken term detection (STD) aims at retrieving data from a speech repository given a textual representation of the search term. Nowadays, it is receiving much interest due to the large volume of multimedia information. STD differs from automatic speech recognition (ASR) in that ASR is interested in all the terms/words that appear in the speech data, whereas STD focuses on a selected list of search terms that must be detected within the speech data. This paper presents the systems submitted to the STD ALBAYZIN 2014 evaluation, held as a part of the ALBAYZIN 2014 evaluation campaign within the context of the IberSPEECH 2014 conference. This is the first STD evaluation that deals with Spanish language. The evaluation consists of retrieving the speech files that contain the search terms, indicating their start and end times within the appropriate speech file, along with a score value that reflects the confidence given to the detection of the search term. The evaluation is conducted on a Spanish spontaneous speech database, which comprises a set of talks from workshops and amounts to about 7 h of speech. We present the database, the evaluation metrics, the systems submitted to the evaluation, the results, and a detailed discussion. Four different research groups took part in the evaluation. Evaluation results show reasonable performance for moderate out-of-vocabulary term rate. This paper compares the systems submitted to the evaluation and makes a deep analysis based on some search term properties (term length, in-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and in-language/foreign terms).