Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models
研究方向由離散語音情感分類轉變爲連續語音情感識別了，首先需要了解這二者的區別。 從分類問題轉爲迴歸問題，以便能夠對連續的情感維度進行建模 片段級別的情感標籤轉換爲時間連續標籤 連續語音情感識別面對的問題： 缺乏可靠的來自評估羣體的黃金標準 面臨訓練模型過程中數據稀缺性的問題 對於問題一，目前常通過最大化相關係數或視聽特徵與情感評分的互信息估計RL模型。對於問題二，文獻中通常使用SVR進行情感預測；
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相關文章
- 1. SPEAKER-AWARE SPEECH-TRANSFORMER
- 2. 2019--Target Speaker Extraction for Multi-Talker Speaker Verification
- 3. 《A tutorial of hidden markov models and selected applications in speech recognition》
- 4. An overview of time series forecasting models
- 5. Extractive Summarization using Continuous Vector Space Models
- 6. Chapter 8 Part-of-Speech Tagging
- 7. Development of Neural Network Models in Text Summarization - 4
- 8. 使用Django Dynamic Models
- 9. Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification
- 10. (30)[ICLR13] Efficient Estimation of Word Representations in Vector Space