Publicly funded community-based physical therapy (PT) services in Canada's most populous province of Ontario were partially delisted, or deinsured, in April 2005. Two previous studies examined the short-term effects from the client and provider perspectives; and in this study, we follow up with participants from these preceding studies to assess long-term consequences of this policy. Sixteen of 18 providers (89%) and 64 of 98 clients (65%) agreed to participate in a follow-up telephone interview. Our results indicate that 12 months following delisting, and despite government assurances that access would be preserved, clients rendered ineligible for publicly funded services report ongoing access barriers across Ontario. Clients in this study also express concern about their overall health and report an increased use of other insured health professionals (e.g., physicians) and services (e.g., hospitals). On the other hand, providers within the network of publicly funded clinics report an important decrease in demand for PT services, whereas those from other settings report little change. We conclude that delisting policies may have long-term consequences on uninsured or underinsured clients and that evidence-based policy planning is warranted to ensure that the goals of reform are aligned with the desired outcomes at the client, provider, and system levels.

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Automatic speech recognition systems usually require large annotated speech corpus for training. The manual annotation of a large corpus is very difficult. It can be very helpful to use unsupervised and semi-supervised learning methods in addition to supervised learning. In this work, we focus on using a semi-supervised training approach for Bangla Speech Recognition that can exploit large unpaired audio and text data. We encode speech and text data in an intermediate domain and propose a novel loss function based on the global encoding distance between encoded data to guide the semisupervised training. Our proposed method reduces the Word Error Rate (WER) of the system from 37% to 31.9%.

Grapheme to phoneme (G2P) conversion is an integral part in various text and speech processing systems, such as: Text to Speech system, Speech Recognition system, etc. The existing methodologies for G2P conversion in Bangla language are mostly rule-based. However, data-driven approaches have proved their superiority over rule-based approaches for largescale G2P conversion in other languages, such as: English, German, etc. As the performance of data-driven approaches for G2P conversion depend largely on pronunciation lexicon on which the system is trained, in this paper, we investigate on developing an improved training lexicon by identifying and categorizing the critical cases in Bangla language and include those critical cases in training lexicon for developing a robust G2P conversion system in Bangla language. Additionally, we have incorporated nasal vowels in our proposed phoneme list. Our methodology outperforms other stateof-the-art approaches for G2P conversion in Bangla language.