Sentence examples for Acoustic query from inspiring English sources

Query-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.

Query-by-example spoken term detection (QbE STD) aims at retrieving data from a speech repository given an acoustic query containing the term of interest as input.

An analysis of the performance of the QbE STD systems (i.e., those with an input acoustic query) based on the length of the queries has been conducted and results are shown in Table 10.

Therefore, QbE STD differs from the STD defined previously, the so-called text-based STD, in that the former uses an acoustic query as input, instead of a text-based representation of the term.

It must be noted that both the difficulty of the task itself (searching acoustic queries on spontaneous data the type and quality of the acoustic data) and the absence of prior knowledge of the target language produce this low performance.

Query-by-example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given an acoustic (spoken) query containing the term of interest as the input.

In addition, a text-based STD system was also presented to compare performance when using written and acoustic (spoken) queries.

Compared to the DDA-1xRT system that handles OOV queries, the combination of acoustic filtering and query-driven decoding provides an absolute F- measure gain of about 8%.

The input to the system is an acoustic example per query; therefore, prior knowledge of the correct word/subword transcription corresponding to each query is not available.

The input to the system is an acoustic example per query, and hence prior knowledge of the correct word/subword transcription corresponding to each query is not available.

Therefore, the input to the system is an acoustic example per query, and hence prior knowledge of the correct word/phone transcription corresponding to each query cannot be used.