Sentence examples for identification language from inspiring English sources

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Over the past few years, there has been a resurgence of interest in designing high-accuracy automatic speech recognition (ASR) systems due to the key rule they can play in many real-world applications, such as voice print for biometric identification, language identification, and call-scanning.

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These questions were mostly factual: major, class standing, contact information, transfer status, gender identification, languages spoken, and cultural/ethnic identification.

The survey will also measure key sociodemographic, experience and training variables, including age, residential location, gender, occupational specialty, the number of years of experience in (1) their current specialty and (2) in cancer care, the number of hours spent weekly in direct patient contact, indigenous identification, languages spoken, country of birth and country of training.

These variables cover four salient features of immigrants' ethnic identity, namely self-identification, language, ethnic interaction, and intentions to return to the country of origin.

Language Identification: Utilizing localized language packs, the natural language search automatically recognizes the user's local language to identify their intent, such as "cuenta de ahorros" versus "savings account".

Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition.

The authors' experiment consisted of classification tests over four typical categories of human voice data, namely, female and male, emotional speech, speaker identification, and language recognition.

Simulation experiments were conducted on classifying four types of voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition into different groups by using SFX and its counterparts (SFX and Feature Selection).

For this purpose, we have designed the Rule Identification Markup Language (RIML), which is similar to the formal Rule Structure Markup Language (RSML), both as parts of XRML.

The identification of language patterns, mostly atypical in patients, requires precision (in terms of language tasks and analysis of data) to correctly describe the reorganized networks and to avoid deficits after surgery.

Early identification of language delay is essential to improve functional outcome in these children.

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