La Brujeria - Issue 3: Into the Under (Once Upon a Time in.

Posted by 2018  •  article

Siri displays entities like dates, times, addresses and currency amounts in a nicely formatted way. This is the result of the application of a process called inverse text normalization (ITN) to the output of a core speech recognition component. To understand the important role ITN plays, consider that, without it, Siri would display “October twenty third twenty sixteen” instead of “October 23, 2016”. In this work, we show that ITN can be formulated as a labelling problem, allowing for the application of a statistical model that is relatively simple, compact, fast to train, and fast to apply. We demonstrate that this approach represents a practical path to a data-driven ITN system.

In most speech recognition systems, a core speech recognizer produces a spoken-form token sequence which is converted to written form through a process called inverse text normalization (ITN). ITN includes formatting entities like numbers, dates, times, and addresses. Table 1 shows examples of spoken-form input and written-form output.

Our goal is to build a data-driven system for ITN. In thinking about how we’d formulate the problem to allow us to apply a statistical model, we could consider naively tokenizing the written-form output by segmenting at spaces. If we were to do that, the output token at a particular position would not necessarily correspond to the input token at that position. Indeed, the number of output tokens would not always be equal to the number of input tokens. At first glance, this problem appears to require a fairly unconstrained sequence-to-sequence model, like those often applied for machine translation.

The latest EU Platform of Diversity Charters meeting, which was hosted by European Commission, DG Justice and Consumers, took place in Brussels on February 4-5. The meeting gathered representatives from the 12 Diversity Charters and diversity experts. The platform meetings provide the opportunity to share best practices and discuss trends and know-how on today’s diversity management.

In November 2015, the Austrian Diversity Charter invited their guests to five bus tours across the city to make diversity touchable.

Diversity is not an end in itself but a means to something greater - to improve competitiveness and the bottom line. In this respect, diversity matters both among employees and among management. Management influences culture and thus helps to promote the gains of diversity.

Siri displays entities like dates, times, addresses and currency amounts in a nicely formatted way. This is the result of the application of a process called inverse text normalization (ITN) to the output of a core speech recognition component. To understand the important role ITN plays, consider that, without it, Siri would display “October twenty third twenty sixteen” instead of “October 23, 2016”. In this work, we show that ITN can be formulated as a labelling problem, allowing for the application of a statistical model that is relatively simple, compact, fast to train, and fast to apply. We demonstrate that this approach represents a practical path to a data-driven ITN system.

In most speech recognition systems, a core speech recognizer produces a spoken-form token sequence which is converted to written form through a process called inverse text normalization (ITN). ITN includes formatting entities like numbers, dates, times, and addresses. Table 1 shows examples of spoken-form input and written-form output.

Our goal is to build a data-driven system for ITN. In thinking about how we’d formulate the problem to allow us to apply a statistical model, we could consider naively tokenizing the written-form output by segmenting at spaces. If we were to do that, the output token at a particular position would not necessarily correspond to the input token at that position. Indeed, the number of output tokens would not always be equal to the number of input tokens. At first glance, this problem appears to require a fairly unconstrained sequence-to-sequence model, like those often applied for machine translation.

Siri displays entities like dates, times, addresses and currency amounts in a nicely formatted way. This is the result of the application of a process called inverse text normalization (ITN) to the output of a core speech recognition component. To understand the important role ITN plays, consider that, without it, Siri would display “October twenty third twenty sixteen” instead of “October 23, 2016”. In this work, we show that ITN can be formulated as a labelling problem, allowing for the application of a statistical model that is relatively simple, compact, fast to train, and fast to apply. We demonstrate that this approach represents a practical path to a data-driven ITN system.

In most speech recognition systems, a core speech recognizer produces a spoken-form token sequence which is converted to written form through a process called inverse text normalization (ITN). ITN includes formatting entities like numbers, dates, times, and addresses. Table 1 shows examples of spoken-form input and written-form output.

Our goal is to build a data-driven system for ITN. In thinking about how we’d formulate the problem to allow us to apply a statistical model, we could consider naively tokenizing the written-form output by segmenting at spaces. If we were to do that, the output token at a particular position would not necessarily correspond to the input token at that position. Indeed, the number of output tokens would not always be equal to the number of input tokens. At first glance, this problem appears to require a fairly unconstrained sequence-to-sequence model, like those often applied for machine translation.

The latest EU Platform of Diversity Charters meeting, which was hosted by European Commission, DG Justice and Consumers, took place in Brussels on February 4-5. The meeting gathered representatives from the 12 Diversity Charters and diversity experts. The platform meetings provide the opportunity to share best practices and discuss trends and know-how on today’s diversity management.

In November 2015, the Austrian Diversity Charter invited their guests to five bus tours across the city to make diversity touchable.

Diversity is not an end in itself but a means to something greater - to improve competitiveness and the bottom line. In this respect, diversity matters both among employees and among management. Management influences culture and thus helps to promote the gains of diversity.

Witchcraft or witchery broadly means the practice of and belief in magical skills and abilities exercised by solitary practitioners and groups. Witchcraft is a broad term that varies culturally and societally, and thus can be difficult to define with precision, [1] therefore cross-cultural assumptions about the meaning or significance of the term should be applied with caution. Witchcraft often occupies a religious divinatory or medicinal role, [2] and is often present within societies and groups whose cultural framework includes a magical world view . [1]

The concept of witchcraft and the belief in its existence have persisted throughout recorded history. They have been present or central at various times and in many diverse forms among cultures and religions worldwide, including both "primitive" and "highly advanced" cultures, [3] and continue to have an important role in many cultures today. [2] Scientifically, the existence of magical powers and witchcraft are generally believed to lack credence and to be unsupported by high quality experimental testing , although individual witchcraft practices and effects may be open to scientific explanation or explained via mentalism and psychology .

The word "witchcraft" derives from the Old English wiccecræft, a compound of "wicce" ("witch") and "cræft" ("craft"). [12]

Siri displays entities like dates, times, addresses and currency amounts in a nicely formatted way. This is the result of the application of a process called inverse text normalization (ITN) to the output of a core speech recognition component. To understand the important role ITN plays, consider that, without it, Siri would display “October twenty third twenty sixteen” instead of “October 23, 2016”. In this work, we show that ITN can be formulated as a labelling problem, allowing for the application of a statistical model that is relatively simple, compact, fast to train, and fast to apply. We demonstrate that this approach represents a practical path to a data-driven ITN system.

In most speech recognition systems, a core speech recognizer produces a spoken-form token sequence which is converted to written form through a process called inverse text normalization (ITN). ITN includes formatting entities like numbers, dates, times, and addresses. Table 1 shows examples of spoken-form input and written-form output.

Our goal is to build a data-driven system for ITN. In thinking about how we’d formulate the problem to allow us to apply a statistical model, we could consider naively tokenizing the written-form output by segmenting at spaces. If we were to do that, the output token at a particular position would not necessarily correspond to the input token at that position. Indeed, the number of output tokens would not always be equal to the number of input tokens. At first glance, this problem appears to require a fairly unconstrained sequence-to-sequence model, like those often applied for machine translation.

The latest EU Platform of Diversity Charters meeting, which was hosted by European Commission, DG Justice and Consumers, took place in Brussels on February 4-5. The meeting gathered representatives from the 12 Diversity Charters and diversity experts. The platform meetings provide the opportunity to share best practices and discuss trends and know-how on today’s diversity management.

In November 2015, the Austrian Diversity Charter invited their guests to five bus tours across the city to make diversity touchable.

Diversity is not an end in itself but a means to something greater - to improve competitiveness and the bottom line. In this respect, diversity matters both among employees and among management. Management influences culture and thus helps to promote the gains of diversity.

Witchcraft or witchery broadly means the practice of and belief in magical skills and abilities exercised by solitary practitioners and groups. Witchcraft is a broad term that varies culturally and societally, and thus can be difficult to define with precision, [1] therefore cross-cultural assumptions about the meaning or significance of the term should be applied with caution. Witchcraft often occupies a religious divinatory or medicinal role, [2] and is often present within societies and groups whose cultural framework includes a magical world view . [1]

The concept of witchcraft and the belief in its existence have persisted throughout recorded history. They have been present or central at various times and in many diverse forms among cultures and religions worldwide, including both "primitive" and "highly advanced" cultures, [3] and continue to have an important role in many cultures today. [2] Scientifically, the existence of magical powers and witchcraft are generally believed to lack credence and to be unsupported by high quality experimental testing , although individual witchcraft practices and effects may be open to scientific explanation or explained via mentalism and psychology .

The word "witchcraft" derives from the Old English wiccecræft, a compound of "wicce" ("witch") and "cræft" ("craft"). [12]

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Posted by 2018  •  article

 
 

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