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Found 6 papers, 1 papers with code
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets
We organize our results in two embedded chipset settings: a) with commodity ARM NEON instruction set and 8-bit containers, we present accuracy, CPU, and memory results using sub 8-bit weights (4, 5, 8-bit) and 8-bit quantization of rest of the network; b) with off-the-shelf neural network accelerators, for a range of weight bit widths (1 and 5-bit), while presenting accuracy results, we project reduction in memory utilization.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
Realizing Petabyte Scale Acoustic Modeling
We present the design and evaluation of a highly scalable and resource efficient SSL system for AM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Lessons from Building Acoustic Models with a Million Hours of Speech
This is a report of our lessons learned building acoustic models from 1 Million hours of unlabeled speech, while labeled speech is restricted to 7, 000 hours.
Data Augmentation for Robust Keyword Spotting under Playback Interference
Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents.
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67. 6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.
