Fast and Accurate OOV Decoder on High-Level Features

This work proposes a novel approach to out-of-vocabulary (OOV) keyword search (KWS) task. The proposed approach is based on using high-level features from an automatic speech recognition (ASR) system, so called phoneme posterior based (PPB) features, for decoding. These features are obtained by calculating time-dependent phoneme posterior probabilities from word lattices, followed by their smoothing. For the PPB features we developed a special novel very fast, simple and efficient OOV decoder. Experimental results are presented on the Georgian language from the IARPA Babel Program, which was the test language in the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum term weighted value (MTWV) metric and computational speed, for single ASR systems, the proposed approach significantly outperforms the state-of-the-art approach based on using in-vocabulary proxies for OOV keywords in the indexed database. The comparison of the two OOV KWS approaches on the fusion results of the nine different ASR systems demonstrates that the proposed OOV decoder outperforms the proxy-based approach in terms of MTWV metric given the comparable processing speed. Other important advantages of the OOV decoder include extremely low memory consumption and simplicity of its implementation and parameter optimization.Comment: Interspeech 2017, August 2017, Stockholm, Sweden. 201

Non-Māori-speaking New Zealanders have a Māori proto-lexicon

We investigate implicit vocabulary learning by adults who are exposed to a language in their ambient environment. Most New Zealanders do not speak Māori, yet are exposed to it throughout their lifetime. We show that this exposure leads to a large proto-lexicon – implicit knowledge of the existence of words and sub-word units without any associated meaning. Despite not explicitly knowing many Māori words, non-Māori-speaking New Zealanders are able to access this proto-lexicon to distinguish Māori words from Māori-like nonwords. What's more, they are able to generalize over the proto-lexicon to generate sophisticated phonotactic knowledge, which lets them evaluate the well-formedness of Māori-like nonwords just as well as fluent Māori speakers

Non-Māori-speaking New Zealanders have a Māori proto-lexicon

We investigate implicit vocabulary learning by adults who are exposed to a language in their ambient environment. Most New Zealanders do not speak Māori, yet are exposed to it throughout their lifetime. We show that this exposure leads to a large proto-lexicon – implicit knowledge of the existence of words and sub-word units without any associated meaning. Despite not explicitly knowing many Māori words, non-Māori-speaking New Zealanders are able to access this proto-lexicon to distinguish Māori words from Māori-like nonwords. What's more, they are able to generalize over the proto-lexicon to generate sophisticated phonotactic knowledge, which lets them evaluate the well-formedness of Māori-like nonwords just as well as fluent Māori speakers

Using pronunciation-based morphological subword units to improve OOV handling in keyword search

Out-of-vocabulary (OOV) keywords present a challenge for keyword search (KWS) systems especially in the low-resource setting. Previous research has centered around approaches that use a variety of subword units to recover OOV words. This paper systematically investigates morphology-based subword modeling approaches on seven low-resource languages. We show that using morphological subword units (morphs) in speech recognition decoding is substantially better than expanding word-decoded lattices into subword units including phones, syllables and morphs. As alternatives to grapheme-based morphs, we apply unsupervised morphology learning to sequences of phonemes, graphones, and syllables. Using one of these phone-based morphs is almost always better than using the grapheme-based morphs, but the particular choice varies with the language. By combining the different methods, a substantial gain is obtained over the best single case for all languages, especially for OOV performance

Using pronunciation-based morphological subword units to improve OOV handling in keyword search

Out-of-vocabulary (OOV) keywords present a challenge for keyword search (KWS) systems especially in the low-resource setting. Previous research has centered around approaches that use a variety of subword units to recover OOV words. This paper systematically investigates morphology-based subword modeling approaches on seven low-resource languages. We show that using morphological subword units (morphs) in speech recognition decoding is substantially better than expanding word-decoded lattices into subword units including phones, syllables and morphs. As alternatives to grapheme-based morphs, we apply unsupervised morphology learning to sequences of phonemes, graphones, and syllables. Using one of these phone-based morphs is almost always better than using the grapheme-based morphs, but the particular choice varies with the language. By combining the different methods, a substantial gain is obtained over the best single case for all languages, especially for OOV performance