Unlock Cutting-Edge Voice AI: Highlights from Alibaba’s Speech & Signal Processing eBook

Smart speakers have sparked a wave of interest because they can listen, see, speak, and sense, making human‑machine interaction a key future trend driven by voice technology.

To help engineers explore practical AI voice applications, Alibaba Technology Release presents the e‑book Alibaba Machine Intelligence: Voice and Signal Processing Selected Papers . The book compiles five peer‑reviewed papers covering speech recognition, speech synthesis, and emotion recognition.

Paper Highlights

1. How Deep Feedforward Sequential Memory Network Boosts Speech Synthesis Speed Fourfold? The authors propose a deep feedforward sequential memory network (DFSMN) that matches the subjective quality of bidirectional LSTM‑based synthesis while using only a quarter of the model size and achieving four times faster generation, ideal for memory‑constrained edge devices.

2. A‑LSTM for More Precise Emotion Recognition addresses the temporal‑dependency limitation of standard LSTM by linearly combining multiple hidden states across time. Applied to emotion detection, A‑LSTM improves recognition accuracy by 5.5% over conventional LSTM.

3. New Model Enabling Machines to Understand Long Utterances introduces an improved DFSMN combined with low‑frame‑rate (LFR) processing (LFR‑DFSMN). This acoustic model outperforms the popular BLSTM on large‑vocabulary English and Chinese tasks, offering superior training speed, smaller parameter count, faster decoding, and lower latency.

4. After 200 Sentences My Voice “Twin” Is Born! presents a linear‑network‑based speaker‑adaptation algorithm that learns a speaker‑specific linear transform. Using only 200 adaptation sentences, the system achieves synthesis quality comparable to models trained with 1,000 sentences.

5. Can I Share Your Feelings? Alibaba’s Voice Emotion Recognition Framework describes a composite framework comprising multiple subsystems that deeply mine emotion‑related cues from input speech, enhancing robustness of emotion detection.

All five papers were accepted by ICASSP 2018, the premier international conference on speech and signal processing, ensuring high scholarly quality.

The e‑book aims to bridge academia and industry, providing concrete case studies, problem‑solving methods, and a solid voice‑technology framework to accelerate the adoption of reliable voice interaction across various sectors.