Response type selection for chat-like spoken dialog systems based on LSTM and multi-task learning

We propose a method of automatically selecting appropriate responses in conversational spoken dialog systems by explicitly determining the correct response type that is needed first, based on a comparison of the user’s input utterance with many other utterances. Response utterances are then generated based on this response type designation (back channel, changing the topic, expanding the topic, etc.). This allows the generation of more appropriate responses than conventional end-to-end approaches, which only use the user’s input to directly generate response utterances. As a response type selector, we propose an LSTM-based encoder–decoder framework utilizing acoustic and linguistic features extracted from input utterances. In order to extract these features more accurately, we utilize not only input utterances but also response utterances in the training corpus. To do so, multi-task learning using multiple decoders is also investigated. To evaluate our proposed method, we conducted experiments using a corpus of dialogs between elderly people and an interviewer. Our proposed method outperformed conventional methods using either a point-wise classifier based on Support Vector Machines, or a single-task learning LSTM. The best performance was achieved when our two response type selectors (one trained using acoustic features, and the other trained using linguistic features) were combined, and multi-task learning was also performed

Accuracy of Length of Virtual Stents in Treatment of Intracranial Wide-Necked Aneurysms.

Background and purposePrecise stent deployment is important for successful treatment of intracranial aneurysms by stent-assisted coiling (SAC). We evaluated the accuracy of virtual stents generated using commercial stent planning software by comparing the length of virtual and actually deployed intracranial laser cut stents on three-dimensional digital subtraction angiography (3D-DSA) images.MethodsWe retrospectively analyzed the data of 75 consecutive cases of intracranial wide-necked aneurysms treated with the SAC technique using laser cut stents. Based on 3D-DSA images acquired by C-arm CT, stent sizing and placement were intraoperatively simulated by a commercial software application. The difference in length of the stents was estimated by measuring proximal discrepancies between the end points of the virtual and actually deployed stents on fused pre-procedural and post-procedural 3D-DSA images. Discrepancies between distal stent end points were manually minimized. The Kruskal-Wallis test was applied to test whether stent location, type, and length had an effect on difference in length between virtual and real stent.ResultsThe median difference in length between virtual and real stents was 1.58 mm with interquartile range 1.12-2.12 mm. There was no evidence for an effect of stent location (p = 0.23), stent type (p = 0.33), or stent length (p = 0.53) on difference in length between virtual and real stents.ConclusionsStent planning software allows 3D simulation of laser cut stents overlain on 3D-DSA images of vessels and may thus be useful for stent selection and deployment of laser cut stents during stent-assisted coiling of intracranial aneurysms

Estimated pretreatment hemodynamic prognostic factors of aneurysm recurrence after endovascular embolization.

BACKGROUND:Hemodynamic factors play important roles in aneurysm recurrence after endovascular treatment. OBJECTIVE:Predicting the risk of recurrence by hemodynamic analysis using an untreated aneurysm model is important because such prediction is required before treatment. METHODS:We retrospectively analyzed hemodynamic factors associated with aneurysm recurrence from pretreatment models of five recurrent and five stable posterior communicating artery (Pcom) aneurysms with no significant differences in aneurysm volume, coil packing density, or sizes of the dome, neck, or Pcom. Hemodynamic factors of velocity ratio, flow rate, pressure ratio, and wall shear stress were investigated. RESULTS:Among the hemodynamic factors investigated, velocity ratio and flow rate of the Pcom showed significant differences between the recurrence group and stable group (0.630 ± 0.062 and 0.926 ± 0.051, P= 0.016; 56.4 ± 8.9 and 121.6 ± 6.7, P= 0.008, respectively). CONCLUSIONS:Our results suggest that hemodynamic factors may be associated with aneurysm recurrence among Pcom aneurysms. Velocity and flow rate in the Pcom may be a pretreatment prognostic factor for aneurysm recurrence after endovascular treatment

Text-to-speech system for low-resource language using cross-lingual transfer learning and data augmentation

Deep learning techniques are currently being applied in automated text-to-speech (TTS) systems, resulting in significant improvements in performance. However, these methods require large amounts of text-speech paired data for model training, and collecting this data is costly. Therefore, in this paper, we propose a single-speaker TTS system containing both a spectrogram prediction network and a neural vocoder for the target language, using only 30 min of target language text-speech paired data for training. We evaluate three approaches for training the spectrogram prediction models of our TTS system, which produce mel-spectrograms from the input phoneme sequence: (1) cross-lingual transfer learning, (2) data augmentation, and (3) a combination of the previous two methods. In the cross-lingual transfer learning method, we used two high-resource language datasets, English (24 h) and Japanese (10 h). We also used 30 min of target language data for training in all three approaches, and for generating the augmented data used for training in methods 2 and 3. We found that using both cross-lingual transfer learning and augmented data during training resulted in the most natural synthesized target speech output. We also compare single-speaker and multi-speaker training methods, using sequential and simultaneous training, respectively. The multi-speaker models were found to be more effective for constructing a single-speaker, low-resource TTS model. In addition, we trained two Parallel WaveGAN (PWG) neural vocoders, one using 13 h of our augmented data with 30 min of target language data and one using the entire 12 h of the original target language dataset. Our subjective AB preference test indicated that the neural vocoder trained with augmented data achieved almost the same perceived speech quality as the vocoder trained with the entire target language dataset. Overall, we found that our proposed TTS system consisting of a spectrogram prediction network and a PWG neural vocoder was able to achieve reasonable performance using only 30 min of target language training data. We also found that by using 3 h of target language data, for training the model and for generating augmented data, our proposed TTS model was able to achieve performance very similar to that of the baseline model, which was trained with 12 h of target language data

Expression of MMP-2, MMP-9 and TIMP-1 in the Wall of Abdominal Aortic Aneurysms

An impaired mechanism of regulatory feedback by matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) has been implicated in the development of abdominal aortic aneurysms (AAAs). This study examined the pathogenesis of AAAs with respect to pathological characteristics and expressions of MMP-2, MMP-9 and TIMP-1. Their expressions were evaluated by immunohistochemistry, competitive polymerase chain reaction (PCR) and Western blotting in a total of 23 consecutive AAAs. The AAA specimens were obtained by surgery, while control specimens were obtained at autopsy. Specimens consisted of 6 patients with small-diameter AAAs (30?45 mm), 17 with medium-large-diameter AAAs (> 45 mm) and 11 controls (17?25 mm). Immunohistochemistry showed MMP-2- and TIMP-1-positive cells mainly in the intima, and MMP-9-positive cells in the intima and adventitia. Competitive PCR showed a significantly higher expression of MMP-2 messenger RNA (mRNA) in the small-diameter AAAs, and higher expressions of MMP-9 mRNA in the small-diameter and medium-large-diameter AAAs than in the controls. The mRNA levels significantly correlated between TIMP-1 and MMP-9, and between MMP-2 and MMP-9 in the AAAs, especially in the medium-large-diameter AAAs. Western blotting revealed the expression of MMPs and TIMP-1 variably in all the specimens examined. These results indicated that MMP-2 and MMP-9 might act cooperatively and play a crucial role in the development of AAAs, and that TIMP-1 inhibits MMP-9 in the AAAs, especially in those medium-large-diameter AAAs

Combination of high-resolution cone beam computed tomography and metal artefact reduction software: a new image fusion technique for evaluating intracranial stent apposition after aneurysm treatment.

We introduce a new imaging technique to improve visualisation of stent apposition after endovascular treatment of brain aneurysms employing high-resolution cone beam CT and three-dimensional digital subtraction angiography. After performing a stent-assisted coil embolisation of brain aneurysm, the image datasets were processed with a metal artefact reduction software followed by the automated image fusion programmes. Two patients who underwent aneurysm coiling using a Neuroform stent were evaluated. The reconstructed 3D images showed a detailed structure of the stent struts and identified malappositions of the deployed stents. Case 1 showed good apposition on the outer curvature side of the carotid siphon, while the inner curvature side showed prominent malapposition. Case 2, with multiple aneurysms, showed good apposition on both outer and inner curvature sides, although inward prolapse of the struts was observed. This new imaging technique may help evaluate stent apposition after the endovascular aneurysm treatment