Search Results for author:
Found 17 papers, 1 papers with code
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
Streaming speech recognition architectures are employed for low-latency, real-time applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
End-to-end spoken language understanding using joint CTC loss and self-supervised, pretrained acoustic encoders
It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition
We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion.
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach
For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition
Very recently, as an alternative to LSTM layers, the Conformer architecture was introduced where the encoder of RNN-T is replaced with a modified Transformer encoder composed of convolutional layers at the frontend and between attention layers.
Compute Cost Amortized Transformer for Streaming ASR
We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A neural prosody encoder for end-ro-end dialogue act classification
Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems.
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding
In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Context-Aware Transformer Transducer for Speech Recognition
We also leverage both BLSTM and pretrained BERT based models to encode contextual data and guide the network training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks
Specifically, the model achieves an accuracy of 77. 87\%, 70. 46\%, and 88. 78\% for the RAVDESS, IEMOCAP, and EMO-DB datasets, respectively.
FANS: Fusing ASR and NLU for on-device SLU
In this paper, we introduce FANS, a new end-to-end SLU model that fuses an ASR audio encoder to a multi-task NLU decoder to infer the intent, slot tags, and slot values directly from a given input audio, obviating the need for transcription.
Ranked #14 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Multi-Channel Transformer Transducer for Speech Recognition
In this paper, we present a novel speech recognition model, Multi-Channel Transformer Transducer (MCTT), which features end-to-end multi-channel training, low computation cost, and low latency so that it is suitable for streaming decoding in on-device speech recognition.
The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input
In this paper, we present the mixtures of speech signals to a popular attention-based neural ASR, known as Listen, Attend, and Spell (LAS), at different target-to-interference ratio (TIR) and measure the phoneme error rate.
End-to-End Multi-Channel Transformer for Speech Recognition
Transformers are powerful neural architectures that allow integrating different modalities using attention mechanisms.
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling
We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling.
Tie Your Embeddings Down: Cross-Modal Latent Spaces for End-to-end Spoken Language Understanding
In this paper, we treat an E2E system as a multi-modal model, with audio and text functioning as its two modalities, and use a cross-modal latent space (CMLS) architecture, where a shared latent space is learned between the `acoustic' and `text' embeddings.
End-to-End Neural Transformer Based Spoken Language Understanding
In this paper, we introduce an end-to-end neural transformer-based SLU model that can predict the variable-length domain, intent, and slots vectors embedded in an audio signal with no intermediate token prediction architecture.
