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Teerera Huya Utaure
Listen, Attend and Spell (LAS) is a 2015 end-to-end speech-recognition model that maps acoustic filter-bank features to character sequences.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
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Pfupiso
Its Listener is a pyramidal recurrent encoder; its Speller is an attention-based recurrent decoder. The original paper reports results for a specific Google voice-search subset and does not establish that LAS outperformed the compared production system.
Kudzika Kwakadzika
The LAS paper defines two components: a Listener, a pyramidal recurrent encoder that accepts filter-bank spectra, and a Speller, an attention-based recurrent decoder that emits characters. The pyramidal encoder reduces sequence length, making attention over the encoded frames more manageable. The paper describes joint learning of recognizer components and reports that its model generated character sequences without a pronunciation dictionary or a separately trained language model. On a subset of Google voice search, it reports 14.1% WER without a language model and 10.3% with language-model rescoring; the compared CLDNN-HMM result was 8.0%. These historical numbers apply to the paper’s specific evaluation.
Kuitwa muNyika yeChokwadi
Use LAS as a historical example of an encoder-decoder recognizer that emits characters from acoustic input.
Inspect how the pyramidal Listener reduces the length of the encoder sequence.
Trace how attention connects each Speller output step to encoder states.
Compare reported WER only within the paper’s stated task and evaluation setup.
Ramangwana Rekuteerera Pinda uye Zviperengo
LAS is useful historically for understanding attention-based end-to-end ASR. Later systems explored different encoders, objectives, and streaming mechanisms; performance and latency must be compared on the same data and decoding conditions.
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Take the Listen Attend and Spell quiz
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What is Listen Attend and Spell?
Listen, Attend and Spell (LAS) is a 2015 end-to-end speech-recognition model that maps acoustic filter-bank features to character sequences. Its Listener is a pyramidal recurrent encoder; its Speller is an attention-based recurrent decoder. The original paper reports results for a specific Google voice-search subset and does not establish that LAS outperformed the compared production system.
Ndezvipi zvikamu zviviri zvakakosha zvemuenzaniso weLAS?
LAS ine 'Mutereri' encoder inogadzirisa odhiyo uye 'Speller' attention-based decoder inoburitsa mavara.
Chii chinonzi Speller muLAS inobuda?
Iyo Speller idecoder inogadzira iyo transcript hunhu nehunhu, kudzidza zviperengo zvakananga.
Sei LAS encoder ichinzi 'pyramidal'?
Imwe neimwe nhanho yepiramidi BLSTM inopedza kureba kwekutevedzana, kupfupisa iyo refu yekuteedzera kutevedzana kuti kutarisisa kugoneke.
Ndechipi chikamu chekare chinodiwa neLAS zvakanyanya HAZVINODA?
Kusiyana nemapaipi ekare eHMM-DNN, LAS inodzidza zvakananga kubva kune odhiyo-kune-mavara maviri maviri pasina duramazwi refoni kana kumanikidza kurongeka.
Ndeipi michina inoita kuti Speller atarise pane yakakodzera chikamu cheodhiyo kune yega yega hunhu?
Meshini yekutarisisa inounganidza huremu pamusoro peiyo encoder nyika, ichigadzira mamiriro vector iyo decoder inoshandisa padanho rega rega.
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