Automatic speech emotion recognition has been a research hotspot in the field of human-computer interaction over the past decade. However, due to the lack of research on the inherent temporal relationship of the speech waveform, the current recognition accuracy needs improvement. To make full use of the difference of emotional saturation between time frames, a novel method is proposed for speech recognition using frame-level speech features combined with attention-based long short-term memory (LSTM) recurrent neural networks. Frame-level speech features were extracted from waveform to replace traditional statistical features, which could preserve the timing relations in the original speech through the sequence of frames. To distinguish emotional saturation in different frames, two improvement strategies are proposed for LSTM based on the attention mechanism: first, the algorithm reduces the computational complexity by modifying the forgetting gate of traditional LSTM without sacrificing performance and second, in the final output of the LSTM, an attention mechanism is applied to both the time and the feature dimension to obtain the information related to the task, rather than using the output from the last iteration of the traditional algorithm. Extensive experiments on the CASIA, eNTERFACE, and GEMEP emotion corpora demonstrate that the performance of the proposed approach is able to outperform the state-of-the-art algorithms reported to date.

No abstract

We analyse the relationship between institutional systems (configurations of countries with similar institutional characteristics) and firm performance. We use a large sample of firms from understudied countries to explore whether the performance impact of these configurations is the same ("equifinality"), whether this holds across different measures of firm performance ("Tversky effect"), and whether some institutional configurations better support foreign-owned firms. We find that it is possible to rank institutional systems according to their impact on firm performance, but the ranking differs according to the performance measure. Although foreign ownership on average confers performance advantages, the magnitude of the impact depends on the configuration. Our findings contribute to the understanding of the importance of institutional similarities across countries, and to the implications of these similarities for the theory of the MNE.

No abstract

No abstract