Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSpeech Emotion Recognition Based on CNN+LSTM Model
التعرف على العاطفة الكلام بناء على نموذج CNN + LSTM
736
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
بسبب شعبية خدمات مساعد الحوار الذكي، أصبح التعرف على عاطفي الكلام أكثر وأكثر أهمية.في التواصل بين البشر والآلات، يمكن للتعرف على العاطفة وتحليل العاطفة تعزيز التفاعل بين الآلات والبشر.تستخدم هذه الدراسة نموذج CNN + LSTM لتنفيذ معالجة العاطفة الكلام (SER) والتنبؤ بها.من النتائج التجريبية، من المعروف أن استخدام نموذج CNN + LSTM يحقق أداء أفضل من استخدام نموذج NN التقليدي.
Due to the popularity of intelligent dialogue assistant services, speech emotion recognition has become more and more important. In the communication between humans and machines, emotion recognition and emotion analysis can enhance the interaction between machines and humans. This study uses the CNN+LSTM model to implement speech emotion recognition (SER) processing and prediction. From the experimental results, it is known that using the CNN+LSTM model achieves better performance than using the traditional NN model.
References used
https://aclanthology.org/
rate research
Read More
Existing works in multimodal affective computing tasks, such as emotion recognition and personality recognition, generally adopt a two-phase pipeline by first extracting feature representations for each single modality with hand crafted algorithms, a
nd then performing end-to-end learning with extracted features. However, the extracted features are fixed and cannot be further fine-tuned on different target tasks, and manually finding feature extracting algorithms does not generalize or scale well to different tasks, which can lead to sub-optimal performance. In this paper, we develop a fully end-to-end model that connects the two phases and optimizes them jointly. In addition, we restructure the current datasets to enable the fully end-to-end training. Furthermore, to reduce the computational overhead brought by the end-to-end model, we introduce a sparse cross-modal attention mechanism for the feature extraction. Experimental results show that our fully end-to-end model significantly surpasses the current state-of-the-art models based on the two-phase pipeline. Moreover, by adding the sparse cross-modal attention, our model can maintain the performance with around half less computation in the feature extraction part of the model.
Dictionary-based methods in sentiment analysis have received scholarly attention recently, the most comprehensive examples of which can be found in English. However, many other languages lack polarity dictionaries, or the existing ones are small in s
ize as in the case of SentiTurkNet, the first and only polarity dictionary in Turkish. Thus, this study aims to extend the content of SentiTurkNet by comparing the two available WordNets in Turkish, namely KeNet and TR-wordnet of BalkaNet. To this end, a current Turkish polarity dictionary has been created relying on 76,825 synsets matching KeNet, where each synset has been annotated with three polarity labels, which are positive, negative and neutral. Meanwhile, the comparison of KeNet and TR-wordnet of BalkaNet has revealed their weaknesses such as the repetition of the same senses, lack of necessary merges of the items belonging to the same synset and the presence of redundant narrower versions of synsets, which are discussed in light of their potential to the improvement of the current lexical databases of Turkish.
The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important
implications on the regulation and expression of emotion. Although a series of methods have been established for multimodal stress detection, limited steps have been taken to explore the underlying inter-dependence between stress and emotion. In this work, we investigate the value of emotion recognition as an auxiliary task to improve stress detection. We propose MUSER -- a transformer-based model architecture and a novel multi-task learning algorithm with speed-based dynamic sampling strategy. Evaluation on the Multimodal Stressed Emotion (MuSE) dataset shows that our model is effective for stress detection with both internal and external auxiliary tasks, and achieves state-of-the-art results.
Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or
customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context. We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.
Transformer models fine-tuned with a sequence labeling objective have become the dominant choice for named entity recognition tasks. However, a self-attention mechanism with unconstrained length can fail to fully capture local dependencies, particula
rly when training data is limited. In this paper, we propose a novel joint training objective which better captures the semantics of words corresponding to the same entity. By augmenting the training objective with a group-consistency loss component we enhance our ability to capture local dependencies while still enjoying the advantages of the unconstrained self-attention mechanism. On the CoNLL2003 dataset, our method achieves a test F1 of 93.98 with a single transformer model. More importantly our fine-tuned CoNLL2003 model displays significant gains in generalization to out of domain datasets: on the OntoNotes subset we achieve an F1 of 72.67 which is 0.49 points absolute better than the baseline, and on the WNUT16 set an F1 of 68.22 which is a gain of 0.48 points. Furthermore, on the WNUT17 dataset we achieve an F1 of 55.85, yielding a 2.92 point absolute improvement.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
