18 research outputs found
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations
is a challenging problem. Handling multi-turn interactions requires the system
to understand various conversational use-cases, such as steering, intent
carryover, disfluencies, entity carryover, and repair. The complexity of this
problem is compounded by the fact that these use-cases mix with each other,
often appearing simultaneously in natural language. This work proposes a
non-autoregressive query rewriting architecture that can handle not only the
five aforementioned tasks, but also complex compositions of these use-cases. We
show that our proposed model has competitive single task performance compared
to the baseline approach, and even outperforms a fine-tuned T5 model in
use-case compositions, despite being 15 times smaller in parameters and 25
times faster in latency.Comment: Interspeech 202
Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΠ°ΠΏΠ»Π°ΡΠ° Π΄Π»Ρ ΡΠΈΠ³ΡΡ Π²ΡΠ°ΡΠ΅Π½ΠΈΡ Ρ ΡΠΎΡΠΌΠ°ΠΌΠΈ ΠΎΡ - 0,0025 Π΄ΠΎ - 0,2500
Detecting Nonnative Speech Using Speaker Recognition Approaches
Detecting whether a talker is speaking his native language is useful for speaker recognition, speech recognition, and intelligence applications. We study the problem of detecting nonnative speakers of American English, using two standard speech corpora. We apply approaches effective in speaker verification to this task, including systems based on MLLR, phone N-gram, prosodic, and word Ngram features. Results show equal error rates between 12 % and 20%, depending on the system, test data, and choice of training data. Asymmetries in performance are most likely explained by differences in native language distributions in the corpora. Model combination yields substantial improvements over individual models, with the best result being around 8.6 % EER. While phone Ngrams are widely used in related tasks (e.g., language and dialect ID), we find that it is the least effective model in combination; MLLR, prosody, and word N-gram systems play stronger roles. Overall, results suggest that individual systems and system combinations found useful for speaker ID also offer promise for nonnativeness detection, and that further efforts are warranted in this area. 1