Sentence examples for retrieval speech from inspiring English sources

Inspired by the great success of deep network in image retrieval, speech recognition, and computer vision [ 13– 21], this paper proposes a deep model framework to deal with the above issues.

Spoken language recognition is an enabling technology for a wide range of intelligence and security applications for information distillation, such as spoken document retrieval, multilingual speech recognition, and spoken language translation [2].

Thus, we follow the general procedure of non-linguistic information retrieval from speech or sound [23, 24].

Instead, the conversation data were scored using Profile of Word Errors and Retrieval in Speech (POWERS, [ 3]).

We analysed these using Profile of Word Errors and Retrieval in Speech (Herbert et al. [ 3]) and investigated a set of conversational variables predicted to change with therapy.

PPA can be further subdivided into progressive non-fluent aphasia (PNFA) where patients have difficulties with word retrieval, non-fluent speech patterns and a progressive loss of speech, and semantic dementia (SD), where there is a loss of memory regarding the understanding of words and objects.

The issue of carryover of improved lexical retrieval to connected speech following lexical therapy also begs the question of what changes in the language processing system might underlie this.

However, it is important to note that what may crucially underpin the carryover of improved verb retrieval to connected speech is the central role that verbs play in determining sentence structure, a role which nouns do not, of course, assume.

The very small number of studies that investigate generalisation of improved noun retrieval to connected speech is presumably due to the complexities presented by trying to measure such carryover.

Both tasks involve processes such as lexical retrieval, search, and speech production but differ in their use of semantic associations, the load they place on search processes, and their use of articulatory processes to identify exemplars (Martin et al. 1994; Schwartz et al. 2003).

There are different speech information retrieval tasks, including spoken document retrieval (SDR), keyword spotting (KWS), spoken term detection (STD), and query-by-example spoken term detection (QbE STD).