23,922 research outputs found
Dual Language Models for Code Switched Speech Recognition
In this work, we present a simple and elegant approach to language modeling
for bilingual code-switched text. Since code-switching is a blend of two or
more different languages, a standard bilingual language model can be improved
upon by using structures of the monolingual language models. We propose a novel
technique called dual language models, which involves building two
complementary monolingual language models and combining them using a
probabilistic model for switching between the two. We evaluate the efficacy of
our approach using a conversational Mandarin-English speech corpus. We prove
the robustness of our model by showing significant improvements in perplexity
measures over the standard bilingual language model without the use of any
external information. Similar consistent improvements are also reflected in
automatic speech recognition error rates.Comment: Accepted at Interspeech 201
Recognizing Voice Over IP: A Robust Front-End for Speech Recognition on the World Wide Web
The Internet Protocol (IP) environment poses two relevant sources of distortion to the speech recognition problem: lossy speech coding and packet loss. In this paper, we propose a new front-end for speech recognition over IP networks. Specifically, we suggest extracting the recognition feature vectors directly from the encoded speech (i.e., the bit stream) instead of decoding it and subsequently extracting the feature vectors. This approach offers two significant benefits. First, the recognition system is only affected by the quantization distortion of the spectral envelope. Thus, we are avoiding the influence of other sources of distortion due to the encoding-decoding process. Second, when packet loss occurs, our front-end becomes more effective since it is not constrained to the error handling mechanism of the codec. We have considered the ITU G.723.1 standard codec, which is one of the most preponderant coding algorithms in voice over IP (VoIP) and compared the proposed front-end with the conventional approach in two automatic speech recognition (ASR) tasks, namely, speaker-independent isolated digit recognition and speaker-independent continuous speech recognition. In general, our approach outperforms the conventional procedure, for a variety of simulated packet loss rates. Furthermore, the improvement is higher as network conditions worsen.Publicad
Exploring Disentanglement with Multilingual and Monolingual VQ-VAE
This work examines the content and usefulness of disentangled phone and
speaker representations from two separately trained VQ-VAE systems: one trained
on multilingual data and another trained on monolingual data. We explore the
multi- and monolingual models using four small proof-of-concept tasks:
copy-synthesis, voice transformation, linguistic code-switching, and
content-based privacy masking. From these tasks, we reflect on how disentangled
phone and speaker representations can be used to manipulate speech in a
meaningful way. Our experiments demonstrate that the VQ representations are
suitable for these tasks, including creating new voices by mixing speaker
representations together. We also present our novel technique to conceal the
content of targeted words within an utterance by manipulating phone VQ codes,
while retaining speaker identity and intelligibility of surrounding words.
Finally, we discuss recommendations for further increasing the viability of
disentangled representations.Comment: Accepted to Speech Synthesis Workshop 2021 (SSW11
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