Modeling DNN as human learner

In previous experiments, human listeners demonstrated that they had the ability to adapt to unheard, ambiguous phonemes after some initial, relatively short exposures. At the same time, previous work in the speech community has shown that pre-trained deep neural network-based (DNN) ASR systems, like humans, also have the ability to adapt to unseen, ambiguous phonemes after retuning their parameters on a relatively small set. In the first part of this thesis, the time-course of phoneme category adaptation in a DNN is investigated in more detail. By retuning the DNNs with more and more tokens with ambiguous sounds and comparing classification accuracy of the ambiguous phonemes in a held-out test across the time-course, we found out that DNNs, like human listeners, also demonstrated fast adaptation: the accuracy curves were step-like in almost all cases, showing very little adaptation after seeing only one (out of ten) training bins. However, unlike our experimental setup mentioned above, in a typical lexically guided perceptual learning experiment, listeners are trained with individual words instead of individual phones, and thus to truly model such a scenario, we would require a model that could take the context of a whole utterance into account. Traditional speech recognition systems accomplish this through the use of hidden Markov models (HMM) and WFST decoding. In recent years, bidirectional long short-term memory (Bi-LSTM) trained under connectionist temporal classification (CTC) criterion has also attracted much attention. In the second part of this thesis, previous experiments on ambiguous phoneme recognition were carried out again on a new Bi-LSTM model, and phonetic transcriptions of words ending with ambiguous phonemes were used as training targets, instead of individual sounds that consisted of a single phoneme. We found out that despite the vastly different architecture, the new model showed highly similar behavior in terms of classification rate over the time course of incremental retuning. This indicated that ambiguous phonemes in a continuous context could also be quickly adapted by neural network-based models. In the last part of this thesis, our pre-trained Dutch Bi-LSTM from the previous part was treated as a Dutch second language learner and was asked to transcribe English utterances in a self-adaptation scheme. In other words, we used the Dutch model to generate phonetic transcriptions directly and retune the model on the transcriptions it generated, although ground truth transcriptions were used to choose a subset of all self-labeled transcriptions. Self-adaptation is of interest as a model of human second language learning, but also has great practical engineering value, e.g., it could be used to adapt speech recognition to a lowr-resource language. We investigated two ways to improve the adaptation scheme, with the first being multi-task learning with articulatory feature detection during training the model on Dutch and self-labeled adaptation, and the second being first letting the model adapt to isolated short words before feeding it with longer utterances.Ope

Attitudes Of Chinese Listed Enterprises Toward Cash Flow Manipulation: A Resource Dependence Perspective

The prevalence of cash flow manipulation has drawn much scholarly attention in China and worldwide, especially since the exposure of the accounting scandals at Enron, WorldCom, and Qwest. Cash flow status also provides a sound basis for corporate valuation. Using a sample of 12,251 firm-year observations from 1999 to 2009, this study thus investigates the attitudes and behavioral patterns of state-owned enterprises (SOEs) and non-SOEs in China toward cash flow manipulation. From a point of departure of resource-dependence theory, we find that non-SOEs tend to manipulate cash flow upward, whereas SOEs are more prone to manipulate cash flow downward. We also demonstrate that non-SOEs are more inclined to manipulate their cash flow statements compared with SOEs. The reason behind this differing behavior could be that non-SOEs are reliant on cash and funds from entities, such as governments and banks, and thus, they falsely enhance cash flow and firm performance in order to signal their solvency and thereby reduce financing costs. By contrast, since SOEs always receive sufficient cash inflows from both government sources and state-owned banks, the managers of these firms are unconcerned about cash flow shortages, which lessens their motivation to manipulate the figures. Indeed, this study finds that these managers may even reduce reported cash flow intentionally in order to obtain government assistance. Therefore, investors and regulators should make their judgments on the cash flow of entities based on their status as SOEs or non-SOEs

The Effects Of Ownership Structure And Listed Status On Bank Risk In China

This paper investigates the relationship of ownership structure, listed status and risk by using regression analysis based on the relevant data of China’s commercial banks. Three main results emerge. First, compared to the state-owned banks, foreign-owned commercial banks exhibit better asset quality, lower credit risk and higher capital adequacy ratio; city commercial banks have lower credit risk and joint-stock commercial banks have lower credit risk and capital adequacy ratio. Second, listed status improves the asset quality and capital adequacy ratio. Finally, we also find that the listed status significantly moderates the relationship between ownership structure and risk. In conclusion, this study provides a theoretical reference for the reform of China’s commercial banks

Firm Performance And Emerging Economies

The study explores the relationship between firm performance, macro-economic variables, and firm size. The analysis was conducted over a period of 12 years, for seven non-financial sectors of Pakistan economy, considering an emerging economy. The analysis was conducted stepwise. First estimation of models considering all co-efficient constant across time and individuals (Sector) was conducted. Secondly, to know the significant difference among the sectors with respect to firm size, return on assets, and earnings per share, we applied LSDV model and kept sectors constant. Lastly, we analyzed the time influence. The results of the study indicate that the size and performance of firms both depend upon financial ratios and macroeconomic variables included in the study. There is significant difference in terms of size and performance between all sectors. There is significant difference in terms of size and performance when measured between 2008 to 2010 and before

Free Cash Flow, Growth Opportunities, And Dividends: Does Cross-Listing Of Shares Matter?

Corporate dividend policy should strike a balance between paying cash to shareholders when there are excess resources and retaining sufficient resources in the company to fund worthwhile projects. Using excess resources to pay dividends can help to avoid overinvestment by the company in inappropriate projects and/or other potential misuse of funds by managers for their own benefit. However, companies also need to avoid paying too much in dividends to ensure that adequate resources are available within the company to fund projects that could increase shareholder wealth (i.e., to avoid underinvestment). Cross-listing of company shares can improve governance and oversight, which may make the dividend policies of cross-listed companies more likely to avoid both over and underinvestment. Using a sample of Chinese listed companies from 2003 to 2011, we find that cross-listed companies pay higher dividends than non-cross-listed companies when there are excess resources (measured by free cash flow), thereby reducing the potential for overinvestment/misuse of the resources by cross-listed companies. We also find that the dividends of cross-listed companies are lower than those of non-cross-listed companies when there are greater growth opportunities (measure by the market-to-book ratio), reflecting the reduced potential for underinvestment by cross-listed companies. We find more limited evidence that cross-listings may influence the relationship between dividend volatility and free cash flow and growth opportunities. Overall, our results suggest that companies cross-listing their shares have dividend policies that are more responsive than those of non-cross-listed companies to potential shareholder concerns about over and underinvestment

Enforcing constraints for multi-lingual and cross-lingual speech-to-text systems

The recent development of neural network-based automatic speech recognition (ASR) systems has greatly reduced the state-of-the-art phone error rates in several languages. However, when an ASR system trained on one language tries to recognize speech from another language, such a system usually fails, even when the two languages come from the same language family. The above scenario poses a problem for low-resource languages. Such languages usually do not have enough paired data for training a moderately-sized ASR model and thus require either cross-lingual adaptation or zero-shot recognition. Due to the increasing interest in bringing ASR technology to low-resource languages, the cross-lingual adaptation of end-to-end speech recognition systems has recently received more attention. However, little analysis has been done to understand how the model learns a shared representation across languages and how language-dependent representations can be fine-tuned to improve the system’s performance. We compare a bi-lingual CTC model with language-specific tuning at earlier LSTM layers to one without such tuning. This is to understand if having language-independent pathways in the model helps with multi-lingual learning and why. We first train the network on Dutch and then transfer the system to English under the bi-lingual CTC loss. After that, the representations from the two networks are visualized. Results showed that the consonants of the two languages are learned very well under a shared mapping but that vowels could benefit significantly when further language-dependent transformations are applied before the last classification layer. These results can be used as a guide for designing multilingual and cross-lingual end-to-end systems in the future. However, creating specialized processing units in the neural network for each training language could yield increasingly large networks as the number of training languages increases. It is also unclear how to adapt such a system to zero-shot recognition. The remaining work adapts two existing constraints to the realm of multi-lingual and cross-lingual ASR. The first constraint is cycle-consistent training. This method defines a shared codebook of phonetic tokens for all training languages. Input speech first passes through the speech encoder of the ASR system and gets quantized into discrete representations from the codebook. The discrete sequence representation is then passed through an auxiliary speech decoder to reconstruct the input speech. The framework constrains the reconstructed speech to be close to the original input speech. The second constraint is regret minimization training. It separates an ASR encoder into two parts: a feature extractor and a predictor. Regret minimization defines an additional regret term for each training sample as the difference between the losses of an auxiliary language-specific predictor with the real language I.D. and a fake language I.D. This constraint enables the feature extractor to learn an invariant speech-to-phone mapping across all languages and could potentially improve the model's generalization ability to new languages

Corporate Philanthropic Giving: Active Responsibility Or Passive Ingratiation? Evidence From Chinese Family-Controlled Listed Companies

This paper examines the impact of political connection on family-controlled listed firms’ philanthropic giving activities toward the 2008 Wenchuan Earthquake in China, and stock price reactions to such activities. Using the 542 Chinese listed companies controlled by private owners as the sample, it was found that firms with political connection are more likely to donate. Besides, focusing on the 244 donating firms, it was found that there is a positive impact of the donation amount on stock price response. What’s more, the positive stock price reactions toward the donation announcement made by firms with political connection are not as strong as that of firms without such connection.  Regression results indicate that although family-controlled firms with political connection are more likely to donate, their activities can not generate as much positive stock price effect as their no-political connection counterparts. These results reveal that both political interferences and market mechanisms have critical impact on corporate philanthropic behavior in China